{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries\Blum & Cowburn - Log File.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 9 Apr 2023, 17:24:26

{com}. do "C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries\Tea Party Primaries - Clean Do File - R&R.do"
{txt}
{com}. ********************************************************************************
. *                                                                              *
. *                    How Local Factions Pressure Parties:                      *
. *          Activist Groups and Primary Contests in the Tea Party Era           *
. *                                                                              *
. *                       Rachel M. Blum & Mike Cowburn                          *
. *                                                                              *
. ********************************************************************************
. 
. *** Load Data ******************************************************************
. cd "C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries" //set to wherever files are stored locally
{res}C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries
{txt}
{com}. use "Blum & Cowburn Data.dta" 
{txt}
{com}. xtset geoid year, delta (2)
{res}{txt}{col 8}panel variable:  {res}geoid (unbalanced)
{txt}{col 9}time variable:  {res}{col 25}year, 2000 to 2018, but with gaps
{txt}{col 17}delta:  {res}2 units
{txt}
{com}. set more off
{txt}
{com}. 
. *** Histogram of TP Groups By District *****************************************
. set scheme cleanplots
{txt}
{com}. histogram number_tp_groups if year == 2016, discrete frequency fcolor(gs0) lcolor(white) lwidth(none) gap(10) ytitle(Number of Districts) ytitle(, size(medsmall)) ylabel(, glwidth(vvvthin) glcolor(gs2) gextend) ymtick(, grid glwidth(vvvthin) glcolor(gs2)) xtitle(Number of Groups) xtitle(, size(medsmall) justification(right)) xlabel(0(5)30, ticks glwidth(vvvthin) glcolor(gs2) gextend) plotregion(fcolor(none) lcolor(none)) // Figure 1
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. 
. *** Contested Primaries By District, TP Presence, Year & Type ******************
. graph bar (count) if primary_type_clean != "None", over(tp_candidate1014, relabel(1 "No TP" 2 "TP")) over(year) bar(1, fcolor(gs2) lcolor(gs2)) bar(2, fcolor(emidblue) lcolor(gs12)) blabel(bar) ytitle(Number of Contested Primaries) ytitle(, size(smal)) ylabel(, nogrid) title(Primaries by TP Candidate Presence, position(11)) subtitle(2010-2014, size(small) position(11)) legend(order(1 "No TP Candidate" 2 "TP Candidate")) graphregion(ifcolor(none)) plotregion(lcolor(none) ifcolor(none)) // Figure 2
{res}{p 0 4 2}
{txt}(note:  named style
smal not found in class
gsize,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. *** Propensity Scores & IPW Construction ***************************************
. psmatch2 tp3interacted white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,915
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    232.76
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2500.0383{txt}{col 49}Pseudo R2{col 67}= {res}    0.0445

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp3interacted{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}white_pct {c |}{col 15}{res}{space 2} .0029929{col 27}{space 2}  .000648{col 38}{space 1}    4.62{col 47}{space 3}0.000{col 55}{space 4} .0017229{col 68}{space 3} .0042629
{txt}median_income {c |}{col 15}{res}{space 2}-3.40e-06{col 27}{space 2} 1.38e-06{col 38}{space 1}   -2.47{col 47}{space 3}0.014{col 55}{space 4}-6.11e-06{col 68}{space 3}-7.03e-07
{txt}{space 3}median_age {c |}{col 15}{res}{space 2} .0515992{col 27}{space 2} .0060829{col 38}{space 1}    8.48{col 47}{space 3}0.000{col 55}{space 4} .0396769{col 68}{space 3} .0635215
{txt}{space 2}density_num {c |}{col 15}{res}{space 2}-.0120618{col 27}{space 2} .0123507{col 38}{space 1}   -0.98{col 47}{space 3}0.329{col 55}{space 4}-.0362687{col 68}{space 3} .0121452
{txt}{space 8}dem08 {c |}{col 15}{res}{space 2}-.3755377{col 27}{space 2} .0425668{col 38}{space 1}   -8.82{col 47}{space 3}0.000{col 55}{space 4}-.4589672{col 68}{space 3}-.2921083
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-1.320021{col 27}{space 2}  .226188{col 38}{space 1}   -5.84{col 47}{space 3}0.000{col 55}{space 4}-1.763341{col 68}{space 3}-.8767003
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 51.4964104   40.3486037   11.1478066   .468391507    23.80
{txt}{col 17}        ATT {c |}{res} 51.4964104   45.8689844   5.62742595    .72374112     7.78
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .148955268  -.098913329   .247868597   .014249249    17.40
{txt}{col 17}        ATT {c |}{res} .148955268   .024523829   .124431438   .020149359     6.18
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

           {c |} psmatch2:
 psmatch2: {c |}   Common
 Treatment {c |}  support
assignment {c |} On suppor {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
 Untreated {c |}{res}     1,523 {txt}{c |}{res}     1,523 
{txt}   Treated {c |}{res}     2,392 {txt}{c |}{res}     2,392 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     3,915 {txt}{c |}{res}     3,915 
{txt}
{com}. psgraph
{res}{txt}
{com}. pstest white_pct median_income median_age density_num dem08

{txt}{hline 24}{c TT}{hline 26}{c TT}{hline 15}{c TT}{hline 10}
        {col 24} {c |}       Mean               {c |}     t-test    {c |}  V(T)/
Variable{col 24} {c |} Treated Control    %bias {c |}    t    p>|t| {c |}  V(C)
{hline 24}{c +}{hline 26}{c +}{hline 15}{c +}{hline 10}
white_pct             {col 24} {c |}{res} 47.998   47.537      1.3{txt} {c |}{res}   0.45  0.654{txt} {c |}{res}  1.09*
{txt}median_income         {col 24} {c |}{res}  54992    54663      2.1{txt} {c |}{res}   0.73  0.465{txt} {c |}{res}  0.92*
{txt}median_age            {col 24} {c |}{res} 37.929   38.148     -5.9{txt} {c |}{res}  -2.15  0.032{txt} {c |}{res}  0.93
{txt}density_num           {col 24} {c |}{res}  3.408   3.3704      2.2{txt} {c |}{res}   0.77  0.440{txt} {c |}{res}  0.73*
{txt}dem08                 {col 24} {c |}{res} .52425   .55518     -6.4{txt} {c |}{res}  -2.15  0.032{txt} {c |}{res}     .
{txt}{hline 24}{c BT}{hline 26}{c BT}{hline 15}{c BT}{hline 10}
* if variance ratio outside [0.92; 1.08]

{hline 70}
Ps R2   LR chi2   p>chi2   MeanBias   MedBias      B       R     %Var 
{hline 70}
{res}0.002     11.22    0.047      3.6       2.2       9.7    0.99{col 67} 75
{txt}{hline 70}
* if B>25%, R outside [0.5; 2]

{com}. drop iwps3_treated
{txt}
{com}. gen iwps3_treated = 1/_pscore if _treated == 1
{txt}(1,523 missing values generated)

{com}. recode iwps3_treated (.=0)
{txt}(iwps3_treated: 1523 changes made)

{com}. drop iwps3_untreated
{txt}
{com}. gen iwps3_untreated = 1/(1-_pscore) if _treated == 0
{txt}(2,392 missing values generated)

{com}. recode iwps3_untreated (. = 0)
{txt}(iwps3_untreated: 2392 changes made)

{com}. drop iwps3
{txt}
{com}. gen iwps3 = iwps3_treated + iwps3_untreated
{txt}
{com}. set scheme cleanplots
{txt}
{com}. twoway (kdensity _pscore if _treated==1, lcolor(gs2)) (kdensity _pscore if _treated==0, lcolor(gs2) lpattern(dash)), legend(label( 1 "Treated") label( 2 "Control" )) xtitle("Propensity scores BEFORE weighting") ytitle("Density") saving(before, replace) plotregion(fcolor(none) lcolor(none))
{res}{txt}(file before.gph saved)

{com}. twoway (kdensity _pscore if _treated==1 [aw=iwps3], lcolor(gs2)) (kdensity _pscore if _treated==0 [aw=iwps3], lcolor(gs2) lpattern(dash)), legend( label( 1 "Treated") label( 2 "Control" )) xtitle("Propensity scores AFTER weighting") ytitle("Density") saving(weight, replace) plotregion(fcolor(none) lcolor(none))
{res}{txt}(file weight.gph saved)

{com}. grc1leg before.gph weight.gph, cols(2) ycommon xcommon subtitle("Before                                                                         After") // Figure 3
{res}{txt}
{com}. save "Blum & Cowburn Data.dta", replace  
{txt}file Blum & Cowburn Data.dta saved

{com}. 
. // Demonstrate Balance (Table 2)
. tabstat iwps3, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: iwps3
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  2.601226  .9822676  2.287199  10.17146   1.30266
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  1.630837  .3178716  1.565331  4.232256  1.123941
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  2.008334  .8128893  1.778633  10.17146  1.123941
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  37.91121  33.62297      33.5      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  47.99778  36.30572  62.92498      95.8      .098
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  44.07394   35.6231      52.9      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  54554.61  16778.47     51647    125675     19311
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  54992.37  15243.99  51699.84    129821     25630
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  54822.07  15857.91   51666.1    129821     19311
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  36.59133  3.955628      36.7      51.1      22.3
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  37.92868  3.468013      37.8      55.7        21
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  37.40843  3.722482      37.4      55.7        21
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  3.443204  1.845518         3         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  3.408027  1.550996         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  3.421711  1.671606         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  .6822062  .4657718         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  .5242475  .4995161         1         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915   .585696  .4926644         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct [aw=iwps3], by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523   44.6073   35.0285        48      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  44.23373   36.3982      56.1      95.8      .098
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  44.42196  35.71015  53.24518      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income [aw=iwps3], by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  54583.93  16216.02     51738    125675     19311
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  54751.43  15365.18  51575.74    129821     25630
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  54667.03  15797.55     51669    129821     19311
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age [aw=iwps3], by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  37.59828  4.049905      37.7      51.1      22.3
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  37.48003  3.542402      37.4      55.7        21
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  37.53961   3.80647      37.5      55.7        21
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num [aw=iwps3], by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  3.415663  1.813842         3         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  3.421446  1.584961         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  3.418532  1.703879         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08 [aw=iwps3], by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  .5700364  .4952332         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  .5804759  .4935843         1         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  .5752159  .4943733         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat _pscore, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: _pscore
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  .5740391  .1208895  .5627839  .9016857  .2323402
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  .6333952  .1074954  .6388424  .8897261  .2362806
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  .6103047  .1165309  .6145821  .9016857  .2323402
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat _pscore [aw=iwps3], by(tp3interacted) statistics(count mean sd median max min) columns(statistics) 

{txt}Summary for variables: _pscore
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  .6155659  .1263915  .6149129  .9016857  .2323402
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392   .613182  .1113533  .6181758  .8897261  .2362806
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  .6143831  .1191561    .61712  .9016857  .2323402
{txt}{hline 14}{c BT}{hline 60}

{com}. covbal tp3interacted white_pct median_income median_age density_num dem08 _pscore


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 47.99778}}}{space 1}{space 1}{ralign 9:{res:{sf: 1318.105}}}{space 1}{space 1}{ralign 9:{res:{sf:-.3183181}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.91121}}}{space 1}{space 1}{ralign 9:{res:{sf: 1130.504}}}{space 1}{space 1}{ralign 9:{res:{sf: .2273559}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2882696}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.165945}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 54992.37}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.32e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.188313}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 54554.61}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.82e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.095125}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0273092}}}{space 1}{space 1}{ralign 9:{res:{sf: .8254529}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.92868}}}{space 1}{space 1}{ralign 9:{res:{sf: 12.02712}}}{space 1}{space 1}{ralign 9:{res:{sf: .2636573}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 36.59133}}}{space 1}{space 1}{ralign 9:{res:{sf: 15.64699}}}{space 1}{space 1}{ralign 9:{res:{sf:-.1317571}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3595191}}}{space 1}{space 1}{ralign 9:{res:{sf: .7686534}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.408027}}}{space 1}{space 1}{ralign 9:{res:{sf:  2.40559}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2629315}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.443204}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.405938}}}{space 1}{space 1}{ralign 9:{res:{sf: .0499716}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0206364}}}{space 1}{space 1}{ralign 9:{res:{sf:  .706293}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5242475}}}{space 1}{space 1}{ralign 9:{res:{sf: .2495164}}}{space 1}{space 1}{ralign 9:{res:{sf:-.0971042}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .6822062}}}{space 1}{space 1}{ralign 9:{res:{sf: .2169434}}}{space 1}{space 1}{ralign 9:{res:{sf:-.7826409}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.3270781}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.150145}}}{space 1}
{space 0}{space 0}{ralign 12:_pscore}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .6333952}}}{space 1}{space 1}{ralign 9:{res:{sf: .0115553}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2524153}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5740391}}}{space 1}{space 1}{ralign 9:{res:{sf: .0146143}}}{space 1}{space 1}{ralign 9:{res:{sf: .1565897}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5188983}}}{space 1}{space 1}{ralign 9:{res:{sf: .7906834}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. covbal tp3interacted white_pct median_income median_age density_num dem08 _pscore, wt(iwps3)


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 44.23373}}}{space 1}{space 1}{ralign 9:{res:{sf: 1324.829}}}{space 1}{space 1}{ralign 9:{res:{sf:-.1404653}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  44.6073}}}{space 1}{space 1}{ralign 9:{res:{sf: 1226.996}}}{space 1}{space 1}{ralign 9:{res:{sf:-.0692032}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0104583}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.079734}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 54751.43}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.36e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.179253}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 54583.93}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.63e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.091498}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0106035}}}{space 1}{space 1}{ralign 9:{res:{sf: .8978149}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.48003}}}{space 1}{space 1}{ralign 9:{res:{sf: 12.54861}}}{space 1}{space 1}{ralign 9:{res:{sf: .0176654}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.59828}}}{space 1}{space 1}{ralign 9:{res:{sf: 16.40173}}}{space 1}{space 1}{ralign 9:{res:{sf:-.0089147}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0310801}}}{space 1}{space 1}{ralign 9:{res:{sf: .7650784}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.421446}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.512101}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2551532}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.415663}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.290021}}}{space 1}{space 1}{ralign 9:{res:{sf: .0525519}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0033953}}}{space 1}{space 1}{ralign 9:{res:{sf: .7635516}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5804759}}}{space 1}{space 1}{ralign 9:{res:{sf: .2436255}}}{space 1}{space 1}{ralign 9:{res:{sf: -.326156}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5700364}}}{space 1}{space 1}{ralign 9:{res:{sf: .2452559}}}{space 1}{space 1}{ralign 9:{res:{sf: -.282935}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0211151}}}{space 1}{space 1}{ralign 9:{res:{sf:  .993352}}}{space 1}
{space 0}{space 0}{ralign 12:_pscore}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .613182}}}{space 1}{space 1}{ralign 9:{res:{sf: .0123996}}}{space 1}{space 1}{ralign 9:{res:{sf:-.1934658}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .6155659}}}{space 1}{space 1}{ralign 9:{res:{sf: .0159748}}}{space 1}{space 1}{ralign 9:{res:{sf:-.0695358}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0200141}}}{space 1}{space 1}{ralign 9:{res:{sf:  .776195}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. 
. *** Parallel Trends ************************************************************
. use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. collapse (mean) recent_presidential_share (sem) se_recent_presidential_share = recent_presidential_share [aweight = iwps3], by(year tp3interacted)
{txt}
{com}. drop if year == 2018 | year == 2006 | year == 2010 | year == 2014
{txt}(8 observations deleted)

{com}. gen upper_ci = recent_presidential_share + (se_recent_presidential_share*1.96)
{txt}
{com}. gen lower_ci = recent_presidential_share - (se_recent_presidential_share*1.96)
{txt}
{com}. reshape wide recent_presidential_share se_recent_presidential_share upper_ci lower_ci, i(year) j(tp3interacted)
{txt}(note: j = 0 1)

Data{col 36}long{col 43}->{col 48}wide
{hline 77}
Number of obs.                 {res}      10   {txt}->{res}       5
{txt}Number of variables            {res}       6   {txt}->{res}       9
{txt}j variable (2 values)     {res}tp3interacted   {txt}->   (dropped)
xij variables:
              {res}recent_presidential_share   {txt}->   {res}recent_presidential_share0 recent_presidential_share1
           se_recent_presidential_share   {txt}->   {res}se_recent_presidential_share0 se_recent_presidential_share1
                               upper_ci   {txt}->   {res}upper_ci0 upper_ci1
                               lower_ci   {txt}->   {res}lower_ci0 lower_ci1
{txt}{hline 77}

{com}. graph twoway (line recent_presidential_share0 year, lcolor(gs6) lpattern(dash) lwidth(thick)) (rcap upper_ci0 lower_ci0 year, lcolor(gs6) lpattern(dash) lwidth(thin)) (line recent_presidential_share1 year, lcolor(gs2) lwidth(thick)) (rcap upper_ci1 lower_ci1 year, lcolor(gs2) lwidth(thin)), legend(order(1 "Control" 3 "Treatment") title("Group", size(small))) ytitle("Presidential Vote Share", size(small)) xtitle("Year (Treatment Period: 2010-2014)", size(small)) ylabel(40(5)55) xlabel(2000(4)2016) xline(2012, lwidth(30) lcolor(gs14%50) lpattern(solid)) // Figure 4
{res}{txt}
{com}. 
. use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. collapse (mean) district_nokkenpoole (sem) se_district_nokkenpoole = district_nokkenpoole [aweight = iwps3], by(year tp3interacted)
{txt}
{com}. drop if year == 2000 | year == 2018
{txt}(4 observations deleted)

{com}. gen upper_ci = district_nokkenpoole + (se_district_nokkenpoole*1.96)
{txt}
{com}. gen lower_ci = district_nokkenpoole - (se_district_nokkenpoole*1.96)
{txt}
{com}. reshape wide district_nokkenpoole se_district_nokkenpoole upper_ci lower_ci, i(year) j(tp3interacted)
{txt}(note: j = 0 1)

Data{col 36}long{col 43}->{col 48}wide
{hline 77}
Number of obs.                 {res}      14   {txt}->{res}       7
{txt}Number of variables            {res}       6   {txt}->{res}       9
{txt}j variable (2 values)     {res}tp3interacted   {txt}->   (dropped)
xij variables:
                   {res}district_nokkenpoole   {txt}->   {res}district_nokkenpoole0 district_nokkenpoole1
                se_district_nokkenpoole   {txt}->   {res}se_district_nokkenpoole0 se_district_nokkenpoole1
                               upper_ci   {txt}->   {res}upper_ci0 upper_ci1
                               lower_ci   {txt}->   {res}lower_ci0 lower_ci1
{txt}{hline 77}

{com}. replace year = 109 in 1
{txt}(1 real change made)

{com}. replace year = 110 in 2
{txt}(1 real change made)

{com}. replace year = 111 in 3
{txt}(1 real change made)

{com}. replace year = 112 in 4
{txt}(1 real change made)

{com}. replace year = 113 in 5
{txt}(1 real change made)

{com}. replace year = 114 in 6
{txt}(1 real change made)

{com}. replace year = 115 in 7
{txt}(1 real change made)

{com}. graph twoway (line district_nokkenpoole0 year, lcolor(gs6) lpattern(dash) lwidth(thick)) (rcap upper_ci0 lower_ci0 year, lcolor(gs6) lpattern(dash) lwidth(thin)) (line district_nokkenpoole1 year, lcolor(gs2) lwidth(thick)) (rcap upper_ci1 lower_ci1 year, lcolor(gs2) lwidth(thin)), legend(order(1 "Control" 3 "Treatment") title("Group", size(small))) ytitle("Legislator Position", size(small)) xtitle("Congress (Treatment Period: 112th–114th Congress)", size(small)) xlabel(109(1)115) xline(113, lwidth(50) lcolor(gs14%50) lpattern(solid)) // Figure 5
{res}{txt}
{com}. 
. *** Analysis  ******************************************************************
. 
. /// Pres Vote Share ///
> use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.643{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}48.383{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}5.740{col 27}{txt}{c |} {res}1.460{col 37}{txt}{c |} {res}3.93{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.019{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}49.791{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}7.772{col 27}{txt}{c |} {res}1.783{col 37}{txt}{c |} {res}4.36{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}2.032{col 27}{txt}{c |} {res}1.182{col 37}{txt}{c |} {res}1.72{col 47}{txt}{c |} {res}0.086*
{txt}{hline 56}
R-square:{res}    0.05
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace // Table 3
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. //Raw Republican Vote as H1 Extension, Mechanism Clarification
. psmatch2 tp3interacted white_pct median_income median_age density_num dem08, out(pres_vote_raw district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}       870
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}     99.29
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-529.72858{txt}{col 49}Pseudo R2{col 67}= {res}    0.0857

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp3interacted{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}white_pct {c |}{col 15}{res}{space 2} .0158229{col 27}{space 2} .0025575{col 38}{space 1}    6.19{col 47}{space 3}0.000{col 55}{space 4} .0108103{col 68}{space 3} .0208355
{txt}median_income {c |}{col 15}{res}{space 2}-2.03e-06{col 27}{space 2} 2.79e-06{col 38}{space 1}   -0.73{col 47}{space 3}0.466{col 55}{space 4}-7.51e-06{col 68}{space 3} 3.44e-06
{txt}{space 3}median_age {c |}{col 15}{res}{space 2} .0247488{col 27}{space 2} .0154652{col 38}{space 1}    1.60{col 47}{space 3}0.110{col 55}{space 4}-.0055624{col 68}{space 3} .0550599
{txt}{space 2}density_num {c |}{col 15}{res}{space 2}-.0007535{col 27}{space 2} .0269444{col 38}{space 1}   -0.03{col 47}{space 3}0.978{col 55}{space 4}-.0535635{col 68}{space 3} .0520565
{txt}{space 8}dem08 {c |}{col 15}{res}{space 2}-.2080119{col 27}{space 2} .0948333{col 38}{space 1}   -2.19{col 47}{space 3}0.028{col 55}{space 4}-.3938817{col 68}{space 3}-.0221422
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-1.414641{col 27}{space 2} .5101685{col 38}{space 1}   -2.77{col 47}{space 3}0.006{col 55}{space 4}-2.414553{col 68}{space 3}-.4147288
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
   pres_vote_raw  Unmatched {c |}{res} 158437.823    113410.35   45027.4725   3516.05488    12.81
{txt}{col 17}        ATT {c |}{res}  158353.28   148494.228   9859.05234   5188.40398     1.90
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .207404851  -.101200599    .30860545   .030572816    10.09
{txt}{col 17}        ATT {c |}{res} .206884112   .116951402    .08993271   .043909897     2.05
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0        334 {txt}{c |}{res}       334 
{txt}   Treated {c |}{res}         1        535 {txt}{c |}{res}       536 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}         1        869 {txt}{c |}{res}       870 
{txt}
{com}. drop iwps3_treated_raw
{txt}
{com}. gen iwps3_treated_raw = 1/_pscore if _treated == 1
{txt}(3,379 missing values generated)

{com}. recode iwps3_treated_raw (.=0)
{txt}(iwps3_treated_raw: 3379 changes made)

{com}. drop iwps3_untreated_raw
{txt}
{com}. gen iwps3_untreated_raw = 1/(1-_pscore) if _treated == 0
{txt}(3,581 missing values generated)

{com}. recode iwps3_untreated_raw (. = 0)
{txt}(iwps3_untreated_raw: 3581 changes made)

{com}. drop iwps3_raw
{txt}
{com}. gen iwps3_raw = iwps3_treated_raw + iwps3_untreated_raw
{txt}
{com}. 
. diff pres_vote_raw if year ==2012 | year==2016 [aweight = iwps3_raw], t(tp3interacted) p(posttreatment_2012) cl(geoid) // raw votes use 2012 (redistricted) districts, hence the assignment here - totals from Daily Kos

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}167{col 28}167{txt}{col 40}334
   Treated:{res}{col 13}268{col 28}268{txt}{col 40}536
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}pres_~w{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com} 1.3e+05{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com} 1.4e+05{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res} 1.0e+04{col 27}{txt}{c |} {res}5340.429{col 37}{txt}{c |} {res}1.94{col 47}{txt}{c |} {res}0.052*
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com} 1.4e+05{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com} 1.5e+05{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res} 1.5e+04{col 27}{txt}{c |} {res}6509.700{col 37}{txt}{c |} {res}2.33{col 47}{txt}{c |} {res}0.020**
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}4791.747{col 27}{txt}{c |} {res}2184.947{col 37}{txt}{c |} {res}2.19{col 47}{txt}{c |} {res}0.029**
{txt}{hline 56}
R-square:{res}    0.02
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Raw) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons replace 
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. reg pres_vote_raw tp3interacted##posttreatment_2012 [aweight = iwps3_raw], cl(geoid)
{txt}(sum of wgt is 1,748.5413236618)

Linear regression                               Number of obs     = {res}       870
                                                {txt}F(3, 434)         =  {res}    31.31
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0190
                                                {txt}Root MSE          =    {res}  53780

{txt}{ralign 98:(Std. Err. adjusted for {res:435} clusters in geoid)}
{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 34}{c |}{col 46}    Robust
{col 1}                   pres_vote_raw{col 34}{c |}      Coef.{col 46}   Std. Err.{col 58}      t{col 66}   P>|t|{col 74}     [95% Con{col 87}f. Interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}1.tp3interacted {c |}{col 34}{res}{space 2} 10386.28{col 46}{space 2} 5340.429{col 57}{space 1}    1.94{col 66}{space 3}0.052{col 74}{space 4}-110.0424{col 87}{space 3}  20882.6
{txt}{space 12}1.posttreatment_2012 {c |}{col 34}{res}{space 2} 5334.045{col 46}{space 2} 1878.606{col 57}{space 1}    2.84{col 66}{space 3}0.005{col 74}{space 4} 1641.747{col 87}{space 3} 9026.342
{txt}{space 32} {c |}
tp3interacted#posttreatment_2012 {c |}
{space 28}1 1  {c |}{col 34}{res}{space 2} 4791.747{col 46}{space 2} 2184.947{col 57}{space 1}    2.19{col 66}{space 3}0.029{col 74}{space 4} 497.3541{col 87}{space 3} 9086.141
{txt}{space 32} {c |}
{space 27}_cons {c |}{col 34}{res}{space 2} 133476.6{col 46}{space 2} 4308.971{col 57}{space 1}   30.98{col 66}{space 3}0.000{col 74}{space 4} 125007.6{col 87}{space 3} 141945.7
{txt}{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. coefplot, drop(_cons) xline(0) label // Figure 6
{res}{txt}
{com}. 
. /// Legislator Position ///
> use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cl(geoid) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.046{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.021{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.067{col 27}{txt}{c |} {res}0.044{col 37}{txt}{c |} {res}1.51{col 47}{txt}{c |} {res}0.132
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.009{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.184{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.193{col 27}{txt}{c |} {res}0.047{col 37}{txt}{c |} {res}4.07{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.126{col 27}{txt}{c |} {res}0.043{col 37}{txt}{c |} {res}2.91{col 47}{txt}{c |} {res}0.004***
{txt}{hline 56}
R-square:{res}    0.04
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Legislator Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) // Table 4
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. // Assessing Adaptation vs. Replacement Using NOMINATE
. diff district_nominate if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cl(geoid) // almost all of the effect is from replacement (0.103 of 0.126)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~te{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.026{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.048{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.074{col 27}{txt}{c |} {res}0.043{col 37}{txt}{c |} {res}1.70{col 47}{txt}{c |} {res}0.090*
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.002{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.179{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.176{col 27}{txt}{c |} {res}0.046{col 37}{txt}{c |} {res}3.83{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.103{col 27}{txt}{c |} {res}0.042{col 37}{txt}{c |} {res}2.43{col 47}{txt}{c |} {res}0.016**
{txt}{hline 56}
R-square:{res}    0.03
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(NOMINATE) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace // Table 5
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. ********************************************************************************
. *                                                                              *
. *                         Supplementary Material                               *
. *                                                                              *
. ********************************************************************************
. 
. ******************* Descriptive Statistics *************************************
. 
. asdoc cor recent_presidential_share district_nokkenpoole tp3interacted tp_candidate1014treat tp_treatment3 median_income white_pct median_age density_num dem08, label replace // Table A1
{txt}(obs=3,045)

             {c |} recent~e distr~le tp3int~d tp_can~t tp_tre~3 media~me white_~t media~ge densit~m    dem08
{hline 13}{c +}{hline 90}
recent_pre~e {c |}{res}   1.0000
{txt}district_~le {c |}{res}   0.7296   1.0000
{txt}tp3interac~d {c |}{res}   0.3562   0.2704   1.0000
{txt}tp_candida~t {c |}{res}   0.2046   0.1312   0.7773   1.0000
{txt}tp_treatme~3 {c |}{res}   0.4740   0.3319   0.6487   0.2260   1.0000
{txt}median_inc~e {c |}{res}  -0.1197  -0.0546   0.0144   0.0269  -0.0020   1.0000
   {txt}white_pct {c |}{res}   0.3060   0.3255   0.2013   0.0786   0.3024   0.1534   1.0000
  {txt}median_age {c |}{res}   0.2136   0.1846   0.1931   0.0703   0.2877   0.1868   0.4484   1.0000
 {txt}density_num {c |}{res}  -0.0726  -0.0392  -0.0050   0.0207  -0.0112   0.1547  -0.0001   0.0750   1.0000
       {txt}dem08 {c |}{res}  -0.4913  -0.5982  -0.1518  -0.0841  -0.1790  -0.0340  -0.1590  -0.0351  -0.0340   1.0000

{txt}(note: file Myfile.doc not found)
Click to Open File:  {browse "Myfile.doc"}

{com}. 
. bys year: asdoc sum district_nokkenpoole recent_presidential_share // Table A2
{txt}(File Myfile.doc already exists, option {bf:append} was assumed)

Summary statistics: N, mean, sd, min, max
  by categories of: __000000 (Election Year)

{ralign 8:__000000} {...}
{c |}{...}
  distr~le  recent~e
{hline 9}{c +}{hline 20}
{ralign 8:2000} {...}
{c |}{...}
 {res}      435       435
{txt}{space 8} {...}
{c |}{...}
 {res} .0462207  47.74023
{txt}{space 8} {...}
{c |}{...}
 {res} .4347279    14.132
{txt}{space 8} {...}
{c |}{...}
 {res}    -.758         5
{txt}{space 8} {...}
{c |}{...}
 {res}     .935        75
{txt}{hline 9}{c +}{hline 20}
{ralign 8:2004} {...}
{c |}{...}
 {res}      435       435
{txt}{space 8} {...}
{c |}{...}
 {res} .0462207  50.14023
{txt}{space 8} {...}
{c |}{...}
 {res} .4347279  14.33507
{txt}{space 8} {...}
{c |}{...}
 {res}    -.758         9
{txt}{space 8} {...}
{c |}{...}
 {res}     .935        78
{txt}{hline 9}{c +}{hline 20}
{ralign 8:2006} {...}
{c |}{...}
 {res}      435       435
{txt}{space 8} {...}
{c |}{...}
 {res} .0017517  50.14023
{txt}{space 8} {...}
{c |}{...}
 {res} .4364372  14.33507
{txt}{space 8} {...}
{c |}{...}
 {res}    -.954         9
{txt}{space 8} {...}
{c |}{...}
 {res}     .975        78
{txt}{hline 9}{c +}{hline 20}
{ralign 8:2008} {...}
{c |}{...}
 {res}      435       435
{txt}{space 8} {...}
{c |}{...}
 {res}-.0173448  45.42275
{txt}{space 8} {...}
{c |}{...}
 {res} .4343931  14.32614
{txt}{space 8} {...}
{c |}{...}
 {res}    -.726  5.203694
{txt}{space 8} {...}
{c |}{...}
 {res}     .991   76.9943
{txt}{hline 9}{c +}{hline 20}
{ralign 8:2010} {...}
{c |}{...}
 {res}      435       435
{txt}{space 8} {...}
{c |}{...}
 {res}  .086508  45.42275
{txt}{space 8} {...}
{c |}{...}
 {res} .4571572  14.32614
{txt}{space 8} {...}
{c |}{...}
 {res}     -.79  5.203694
{txt}{space 8} {...}
{c |}{...}
 {res}     .919   76.9943
{txt}{hline 9}{c +}{hline 20}
{ralign 8:2012} {...}
{c |}{...}
 {res}      435       435
{txt}{space 8} {...}
{c |}{...}
 {res} .0846276  46.91364
{txt}{space 8} {...}
{c |}{...}
 {res} .4568995  15.71654
{txt}{space 8} {...}
{c |}{...}
 {res}    -.731  3.000638
{txt}{space 8} {...}
{c |}{...}
 {res}     .991  80.20388
{txt}{hline 9}{c +}{hline 20}
{ralign 8:2014} {...}
{c |}{...}
 {res}      435       435
{txt}{space 8} {...}
{c |}{...}
 {res}  .101469  46.91364
{txt}{space 8} {...}
{c |}{...}
 {res} .4577194  15.71654
{txt}{space 8} {...}
{c |}{...}
 {res}    -.765  3.000638
{txt}{space 8} {...}
{c |}{...}
 {res}     .991  80.20388
{txt}{hline 9}{c +}{hline 20}
{ralign 8:2016} {...}
{c |}{...}
 {res}      435       435
{txt}{space 8} {...}
{c |}{...}
 {res} .0932299  45.87204
{txt}{space 8} {...}
{c |}{...}
 {res} .4701438  16.79656
{txt}{space 8} {...}
{c |}{...}
 {res}    -.761  4.896464
{txt}{space 8} {...}
{c |}{...}
 {res}        1  80.37155
{txt}{hline 9}{c +}{hline 20}
{ralign 8:2018} {...}
{c |}{...}
 {res}      435       435
{txt}{space 8} {...}
{c |}{...}
 {res} .0300897  45.87204
{txt}{space 8} {...}
{c |}{...}
 {res} .4641531  16.79656
{txt}{space 8} {...}
{c |}{...}
 {res}    -.726  4.896464
{txt}{space 8} {...}
{c |}{...}
 {res}        1  80.37155
{txt}{hline 9}{c +}{hline 20}
{ralign 8:Total} {...}
{c |}{...}
 {res}     3915      3915
{txt}{space 8} {...}
{c |}{...}
 {res} .0525303  47.15973
{txt}{space 8} {...}
{c |}{...}
 {res} .4511028  15.28536
{txt}{space 8} {...}
{c |}{...}
 {res}    -.954  3.000638
{txt}{space 8} {...}
{c |}{...}
 {res}        1  80.37155
{txt}{hline 9}{c BT}{hline 20}
{res}Click to Open File:  {browse "Myfile.doc"}
{txt}
{com}. asdoc corr district_nokkenpoole recent_presidential_share if year == 2008 & tp3interacted == 1
{txt}(File Myfile.doc already exists, option {bf:append} was assumed)
(obs=264)

             {c |} distr~le recent~e
{hline 13}{c +}{hline 18}
district_~le {c |}{res}   1.0000
{txt}recent_pre~e {c |}{res}   0.5249   1.0000

Click to Open File:  {browse "Myfile.doc"}
{txt}
{com}. asdoc corr district_nokkenpoole recent_presidential_share if year == 2008 & tp3interacted == 0
{txt}(File Myfile.doc already exists, option {bf:append} was assumed)
(obs=171)

             {c |} distr~le recent~e
{hline 13}{c +}{hline 18}
district_~le {c |}{res}   1.0000
{txt}recent_pre~e {c |}{res}   0.6077   1.0000

Click to Open File:  {browse "Myfile.doc"}
{txt}
{com}. asdoc corr district_nokkenpoole recent_presidential_share if year == 2016 & tp3interacted == 1
{txt}(File Myfile.doc already exists, option {bf:append} was assumed)
(obs=268)

             {c |} distr~le recent~e
{hline 13}{c +}{hline 18}
district_~le {c |}{res}   1.0000
{txt}recent_pre~e {c |}{res}   0.8098   1.0000

Click to Open File:  {browse "Myfile.doc"}
{txt}
{com}. asdoc corr district_nokkenpoole recent_presidential_share if year == 2016 & tp3interacted == 0
{txt}(File Myfile.doc already exists, option {bf:append} was assumed)
(obs=167)

             {c |} distr~le recent~e
{hline 13}{c +}{hline 18}
district_~le {c |}{res}   1.0000
{txt}recent_pre~e {c |}{res}   0.7881   1.0000

Click to Open File:  {browse "Myfile.doc"}
{txt}
{com}. 
. twoway (scatter pres_vote_change_0816 district_nokken_poole_0816 if year == 2016 & tp3interacted == 1) ///
>         (scatter pres_vote_change_0816 district_nokken_poole_0816 if year == 2016 & tp3interacted == 0) ///
>         (lfit pres_vote_change_0816 district_nokken_poole_0816 if year == 2016 & tp3interacted == 1) ///
>         (lfit pres_vote_change_0816 district_nokken_poole_0816 if year == 2016 & tp3interacted == 0), ytitle(Change in Pres Vote Share, size(small)) ylabel(, nogrid) title(Correlation Between Change in Dependent Variables, position(11)) subtitle(2008-2016,        size(small) position(11)) legend(order(1 "Treatment Districts" 2 "Control Districts" 3 "Treatment (Fitted)" 4 "Control (Fitted)")) graphregion(ifcolor(none)) plotregion(lcolor(none) ifcolor(none)) // Figure A1
{res}{txt}
{com}. 
. scatter number_tp_groups recent_presidential_share if year ==2008 | year == 2016, xtitle(% Presidential Vote Share, size(small)) ytitle(Number of Tea Party Groups, size(small)) ylabel(, nogrid) title(Correlation Between Number of Tea Party Groups and Presidential Vote Share, size(medium-small)position(11)) subtitle(2008-2016, size(small) position(11)) graphregion(ifcolor(none)) plotregion(lcolor(none) ifcolor(none)) yscale(lcolor(gs12)) ylabel(, tlcolor(gs12)) xscale(lcolor(gs12)) xlabel(, labcolor(black) tlcolor(gs12)) // Figure A2
{p 0 4 2}
{txt}(note:  named style
medium-small not found in class
gsize,  default attributes used)
{p_end}
{res}{txt}
{com}. 
. graph bar (count) if year < 2016 & year > 2008 & primary_type_clean != "None", over(tp_candidate1014) over(primary_type_clean, label(labsize(vsmall))) over(year) bar(1, fcolor(gs2) lcolor(gs2)) bar(2, fcolor(gs12) lcolor(gs12)) ytitle(Number of Contested Primaries) ytitle(, size(small)) title(Primaries by Type & TP Candidate Presence, position(11)) subtitle(2010-2014, size(small) position(11)) legend(order(1 "No TP Candidate" 2 "TP Candidate")) graphregion(ifcolor(none)) plotregion(lcolor(none) ifcolor(none)) // Figure A3
{res}{txt}
{com}. 
. ******************* District Assignment ****************************************
. 
. /// Presentation of 'Raw' PTA ///
> 
. //H1
. use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. collapse (mean) recent_presidential_share (sem) se_recent_presidential_share = recent_presidential_share, by(year tp3interacted)
{txt}
{com}. drop if year == 2018 | year == 2006 | year == 2010 | year == 2014
{txt}(8 observations deleted)

{com}. gen upper_ci = recent_presidential_share + (se_recent_presidential_share*1.96)
{txt}
{com}. gen lower_ci = recent_presidential_share - (se_recent_presidential_share*1.96)
{txt}
{com}. reshape wide recent_presidential_share se_recent_presidential_share upper_ci lower_ci, i(year) j(tp3interacted)
{txt}(note: j = 0 1)

Data{col 36}long{col 43}->{col 48}wide
{hline 77}
Number of obs.                 {res}      10   {txt}->{res}       5
{txt}Number of variables            {res}       6   {txt}->{res}       9
{txt}j variable (2 values)     {res}tp3interacted   {txt}->   (dropped)
xij variables:
              {res}recent_presidential_share   {txt}->   {res}recent_presidential_share0 recent_presidential_share1
           se_recent_presidential_share   {txt}->   {res}se_recent_presidential_share0 se_recent_presidential_share1
                               upper_ci   {txt}->   {res}upper_ci0 upper_ci1
                               lower_ci   {txt}->   {res}lower_ci0 lower_ci1
{txt}{hline 77}

{com}. graph twoway (line recent_presidential_share0 year, lcolor(gs6) lpattern(dash) lwidth(thick)) (rcap upper_ci0 lower_ci0 year, lcolor(gs6) lpattern(dash) lwidth(thin)) (line recent_presidential_share1 year, lcolor(gs2) lwidth(thick)) (rcap upper_ci1 lower_ci1 year, lcolor(gs2) lwidth(thin)), legend(order(1 "Control" 3 "Treatment") title("Group", size(small))) ytitle("Presidential Vote Share", size(small)) xtitle("Year (Treatment Period: 2010-2014)", size(small)) ylabel(35(5)55) xlabel(2000(4)2016) xline(2012, lwidth(30) lcolor(gs14%50) lpattern(solid)) // Figure B1
{res}{txt}
{com}. 
. //H2
. use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. collapse (mean) district_nokkenpoole (sem) se_district_nokkenpoole = district_nokkenpoole, by(year tp3interacted)
{txt}
{com}. drop if year == 2000 | year == 2018
{txt}(4 observations deleted)

{com}. gen upper_ci = district_nokkenpoole + (se_district_nokkenpoole*1.96)
{txt}
{com}. gen lower_ci = district_nokkenpoole - (se_district_nokkenpoole*1.96)
{txt}
{com}. reshape wide district_nokkenpoole se_district_nokkenpoole upper_ci lower_ci, i(year) j(tp3interacted)
{txt}(note: j = 0 1)

Data{col 36}long{col 43}->{col 48}wide
{hline 77}
Number of obs.                 {res}      14   {txt}->{res}       7
{txt}Number of variables            {res}       6   {txt}->{res}       9
{txt}j variable (2 values)     {res}tp3interacted   {txt}->   (dropped)
xij variables:
                   {res}district_nokkenpoole   {txt}->   {res}district_nokkenpoole0 district_nokkenpoole1
                se_district_nokkenpoole   {txt}->   {res}se_district_nokkenpoole0 se_district_nokkenpoole1
                               upper_ci   {txt}->   {res}upper_ci0 upper_ci1
                               lower_ci   {txt}->   {res}lower_ci0 lower_ci1
{txt}{hline 77}

{com}. replace year = 109 in 1
{txt}(1 real change made)

{com}. replace year = 110 in 2
{txt}(1 real change made)

{com}. replace year = 111 in 3
{txt}(1 real change made)

{com}. replace year = 112 in 4
{txt}(1 real change made)

{com}. replace year = 113 in 5
{txt}(1 real change made)

{com}. replace year = 114 in 6
{txt}(1 real change made)

{com}. replace year = 115 in 7
{txt}(1 real change made)

{com}. graph twoway (line district_nokkenpoole0 year, lcolor(gs6) lpattern(dash) lwidth(thick)) (rcap upper_ci0 lower_ci0 year, lcolor(gs6) lpattern(dash) lwidth(thin)) (line district_nokkenpoole1 year, lcolor(gs2) lwidth(thick)) (rcap upper_ci1 lower_ci1 year, lcolor(gs2) lwidth(thin)), legend(order(1 "Control" 3 "Treatment") title("Group", size(small))) ytitle("Legislator Position", size(small)) xtitle("Congress (Treatment Period: 112th–114th Congress)", size(small)) xlabel(109(1)115) xline(113, lwidth(50) lcolor(gs14%50) lpattern(solid)) // Figure B2
{res}{txt}
{com}. 
. 
. /// IPW With Other Numbers of TP Groups ///
> use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. //Generate New Propensity Scores
. psmatch2 tp1interacted white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,045
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}     63.45
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1821.9565{txt}{col 49}Pseudo R2{col 67}= {res}    0.0171

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp1interacted{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}white_pct {c |}{col 15}{res}{space 2}   .00335{col 27}{space 2}  .000882{col 38}{space 1}    3.80{col 47}{space 3}0.000{col 55}{space 4} .0016214{col 68}{space 3} .0050787
{txt}median_income {c |}{col 15}{res}{space 2} 4.35e-07{col 27}{space 2} 1.54e-06{col 38}{space 1}    0.28{col 47}{space 3}0.778{col 55}{space 4}-2.59e-06{col 68}{space 3} 3.46e-06
{txt}{space 3}median_age {c |}{col 15}{res}{space 2} .0129641{col 27}{space 2} .0076822{col 38}{space 1}    1.69{col 47}{space 3}0.091{col 55}{space 4}-.0020928{col 68}{space 3} .0280209
{txt}{space 2}density_num {c |}{col 15}{res}{space 2} -.000888{col 27}{space 2} .0144638{col 38}{space 1}   -0.06{col 47}{space 3}0.951{col 55}{space 4}-.0292365{col 68}{space 3} .0274605
{txt}{space 8}dem08 {c |}{col 15}{res}{space 2}-.2457206{col 27}{space 2} .0501364{col 38}{space 1}   -4.90{col 47}{space 3}0.000{col 55}{space 4}-.3439862{col 68}{space 3} -.147455
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} -.014876{col 27}{space 2} .2737861{col 38}{space 1}   -0.05{col 47}{space 3}0.957{col 55}{space 4} -.551487{col 68}{space 3} .5217349
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 49.1295488   40.7993732   8.33017567   .596744635    13.96
{txt}{col 17}        ATT {c |}{res} 49.1295488    44.998905   4.13064385   .896972028     4.61
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .103776064  -.062398455   .166174518   .017817427     9.33
{txt}{col 17}        ATT {c |}{res} .103776064   .054637214    .04913885   .023984787     2.05
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

           {c |} psmatch2:
 psmatch2: {c |}   Common
 Treatment {c |}  support
assignment {c |} On suppor {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
 Untreated {c |}{res}       906 {txt}{c |}{res}       906 
{txt}   Treated {c |}{res}     2,139 {txt}{c |}{res}     2,139 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     3,045 {txt}{c |}{res}     3,045 
{txt}
{com}. drop iwps1_treated
{txt}
{com}. gen iwps1_treated = 1/_pscore if _treated == 1
{txt}(1,776 missing values generated)

{com}. recode iwps1_treated (.=0)
{txt}(iwps1_treated: 1776 changes made)

{com}. drop iwps1_untreated
{txt}
{com}. gen iwps1_untreated = 1/(1-_pscore) if _treated == 0
{txt}(3,009 missing values generated)

{com}. recode iwps1_untreated (. = 0)
{txt}(iwps1_untreated: 3009 changes made)

{com}. drop iwps1
{txt}
{com}. gen iwps1 = iwps1_treated + iwps1_untreated
{txt}
{com}. 
. psmatch2 tp2interacted white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,045
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    131.25
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res} -1872.559{txt}{col 49}Pseudo R2{col 67}= {res}    0.0339

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp2interacted{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}white_pct {c |}{col 15}{res}{space 2} .0036288{col 27}{space 2} .0008718{col 38}{space 1}    4.16{col 47}{space 3}0.000{col 55}{space 4} .0019201{col 68}{space 3} .0053375
{txt}median_income {c |}{col 15}{res}{space 2} 1.74e-06{col 27}{space 2} 1.54e-06{col 38}{space 1}    1.13{col 47}{space 3}0.257{col 55}{space 4}-1.27e-06{col 68}{space 3} 4.75e-06
{txt}{space 3}median_age {c |}{col 15}{res}{space 2} .0369633{col 27}{space 2} .0076834{col 38}{space 1}    4.81{col 47}{space 3}0.000{col 55}{space 4}  .021904{col 68}{space 3} .0520225
{txt}{space 2}density_num {c |}{col 15}{res}{space 2}-.0076505{col 27}{space 2} .0142659{col 38}{space 1}   -0.54{col 47}{space 3}0.592{col 55}{space 4} -.035611{col 68}{space 3} .0203101
{txt}{space 8}dem08 {c |}{col 15}{res}{space 2}-.2989821{col 27}{space 2} .0495183{col 38}{space 1}   -6.04{col 47}{space 3}0.000{col 55}{space 4}-.3960362{col 68}{space 3} -.201928
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-1.050519{col 27}{space 2} .2731994{col 38}{space 1}   -3.85{col 47}{space 3}0.000{col 55}{space 4} -1.58598{col 68}{space 3}-.5150582
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 50.0449539   39.8631337   10.1818202   .567723146    17.93
{txt}{col 17}        ATT {c |}{res} 50.0449539   44.6790985   5.36585538   .876654482     6.12
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .125834483  -.088669951   .214504433   .017087966    12.55
{txt}{col 17}        ATT {c |}{res} .125834483   .024104433   .101730049   .023231603     4.38
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

           {c |} psmatch2:
 psmatch2: {c |}   Common
 Treatment {c |}  support
assignment {c |} On suppor {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
 Untreated {c |}{res}     1,015 {txt}{c |}{res}     1,015 
{txt}   Treated {c |}{res}     2,030 {txt}{c |}{res}     2,030 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     3,045 {txt}{c |}{res}     3,045 
{txt}
{com}. drop iwps2_treated
{txt}
{com}. gen iwps2_treated = 1/_pscore if _treated == 1
{txt}(1,885 missing values generated)

{com}. recode iwps2_treated (.=0)
{txt}(iwps2_treated: 1885 changes made)

{com}. drop iwps2_untreated
{txt}
{com}. gen iwps2_untreated = 1/(1-_pscore) if _treated == 0
{txt}(2,900 missing values generated)

{com}. recode iwps2_untreated (. = 0)
{txt}(iwps2_untreated: 2900 changes made)

{com}. drop iwps2
{txt}
{com}. gen iwps2 = iwps2_treated + iwps2_untreated
{txt}
{com}. 
. psmatch2 tp4interacted white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,045
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    309.60
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1940.5338{txt}{col 49}Pseudo R2{col 67}= {res}    0.0739

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp4interacted{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}white_pct {c |}{col 15}{res}{space 2} .0060294{col 27}{space 2} .0008578{col 38}{space 1}    7.03{col 47}{space 3}0.000{col 55}{space 4} .0043481{col 68}{space 3} .0077106
{txt}median_income {c |}{col 15}{res}{space 2}-5.38e-06{col 27}{space 2} 1.51e-06{col 38}{space 1}   -3.57{col 47}{space 3}0.000{col 55}{space 4}-8.34e-06{col 68}{space 3}-2.42e-06
{txt}{space 3}median_age {c |}{col 15}{res}{space 2} .0643348{col 27}{space 2}   .00769{col 38}{space 1}    8.37{col 47}{space 3}0.000{col 55}{space 4} .0492627{col 68}{space 3} .0794068
{txt}{space 2}density_num {c |}{col 15}{res}{space 2} .0087153{col 27}{space 2} .0141189{col 38}{space 1}    0.62{col 47}{space 3}0.537{col 55}{space 4}-.0189572{col 68}{space 3} .0363878
{txt}{space 8}dem08 {c |}{col 15}{res}{space 2}-.4078333{col 27}{space 2} .0482841{col 38}{space 1}   -8.45{col 47}{space 3}0.000{col 55}{space 4}-.5024683{col 68}{space 3}-.3131983
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-2.113465{col 27}{space 2} .2753137{col 38}{space 1}   -7.68{col 47}{space 3}0.000{col 55}{space 4} -2.65307{col 68}{space 3} -1.57386
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 52.1134192   39.9725255   12.1408937   .521105725    23.30
{txt}{col 17}        ATT {c |}{res} 52.1134192   45.0872182   7.02620098    .83849643     8.38
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .184613134  -.104951095   .289564229   .015754598    18.38
{txt}{col 17}        ATT {c |}{res} .184613134   .038749254   .145863881    .02353708     6.20
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

           {c |} psmatch2:
 psmatch2: {c |}   Common
 Treatment {c |}  support
assignment {c |} On suppor {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
 Untreated {c |}{res}     1,370 {txt}{c |}{res}     1,370 
{txt}   Treated {c |}{res}     1,675 {txt}{c |}{res}     1,675 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     3,045 {txt}{c |}{res}     3,045 
{txt}
{com}. drop iwps4_treated
{txt}
{com}. gen iwps4_treated = 1/_pscore if _treated == 1
{txt}(2,240 missing values generated)

{com}. recode iwps4_treated (.=0)
{txt}(iwps4_treated: 2240 changes made)

{com}. drop iwps4_untreated
{txt}
{com}. gen iwps4_untreated = 1/(1-_pscore) if _treated == 0
{txt}(2,545 missing values generated)

{com}. recode iwps4_untreated (. = 0)
{txt}(iwps4_untreated: 2545 changes made)

{com}. drop iwps4
{txt}
{com}. gen iwps4 = iwps4_treated + iwps4_untreated
{txt}
{com}. 
. psmatch2 tp5interacted white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,045
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    332.48
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1937.3515{txt}{col 49}Pseudo R2{col 67}= {res}    0.0790

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp5interacted{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}white_pct {c |}{col 15}{res}{space 2} .0065104{col 27}{space 2} .0008621{col 38}{space 1}    7.55{col 47}{space 3}0.000{col 55}{space 4} .0048207{col 68}{space 3} .0082002
{txt}median_income {c |}{col 15}{res}{space 2}-8.70e-06{col 27}{space 2} 1.53e-06{col 38}{space 1}   -5.67{col 47}{space 3}0.000{col 55}{space 4}-.0000117{col 68}{space 3}-5.69e-06
{txt}{space 3}median_age {c |}{col 15}{res}{space 2} .0698216{col 27}{space 2} .0076863{col 38}{space 1}    9.08{col 47}{space 3}0.000{col 55}{space 4} .0547568{col 68}{space 3} .0848864
{txt}{space 2}density_num {c |}{col 15}{res}{space 2} .0184386{col 27}{space 2} .0143689{col 38}{space 1}    1.28{col 47}{space 3}0.199{col 55}{space 4} -.009724{col 68}{space 3} .0466012
{txt}{space 8}dem08 {c |}{col 15}{res}{space 2}-.3590612{col 27}{space 2} .0480147{col 38}{space 1}   -7.48{col 47}{space 3}0.000{col 55}{space 4}-.4531683{col 68}{space 3}-.2649541
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-2.448896{col 27}{space 2} .2767563{col 38}{space 1}   -8.85{col 47}{space 3}0.000{col 55}{space 4}-2.991328{col 68}{space 3}-1.906464
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 53.0470167   41.0692622   11.9777544   .520676988    23.00
{txt}{col 17}        ATT {c |}{res} 53.0304524   46.9064498   6.12400258   .778317638     7.87
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .210357294  -.081828413   .292185707   .015690983    18.62
{txt}{col 17}        ATT {c |}{res} .209827805   .071397318   .138430487   .023637147     5.86
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0      1,626 {txt}{c |}{res}     1,626 
{txt}   Treated {c |}{res}         2      1,417 {txt}{c |}{res}     1,419 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}         2      3,043 {txt}{c |}{res}     3,045 
{txt}
{com}. drop iwps5_treated
{txt}
{com}. gen iwps5_treated = 1/_pscore if _treated == 1
{txt}(2,496 missing values generated)

{com}. recode iwps5_treated (.=0)
{txt}(iwps5_treated: 2496 changes made)

{com}. drop iwps5_untreated
{txt}
{com}. gen iwps5_untreated = 1/(1-_pscore) if _treated == 0
{txt}(2,289 missing values generated)

{com}. recode iwps5_untreated (. = 0)
{txt}(iwps5_untreated: 2289 changes made)

{com}. drop iwps5
{txt}
{com}. gen iwps5 = iwps5_treated + iwps5_untreated
{txt}
{com}. 
. psmatch2 tp6interacted white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,045
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    370.94
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1885.7273{txt}{col 49}Pseudo R2{col 67}= {res}    0.0895

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp6interacted{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}white_pct {c |}{col 15}{res}{space 2} .0069919{col 27}{space 2} .0008744{col 38}{space 1}    8.00{col 47}{space 3}0.000{col 55}{space 4} .0052781{col 68}{space 3} .0087056
{txt}median_income {c |}{col 15}{res}{space 2}-7.41e-06{col 27}{space 2} 1.56e-06{col 38}{space 1}   -4.75{col 47}{space 3}0.000{col 55}{space 4}-.0000105{col 68}{space 3}-4.35e-06
{txt}{space 3}median_age {c |}{col 15}{res}{space 2} .0626894{col 27}{space 2} .0077198{col 38}{space 1}    8.12{col 47}{space 3}0.000{col 55}{space 4} .0475589{col 68}{space 3} .0778199
{txt}{space 2}density_num {c |}{col 15}{res}{space 2} .0306428{col 27}{space 2} .0146774{col 38}{space 1}    2.09{col 47}{space 3}0.037{col 55}{space 4} .0018757{col 68}{space 3} .0594099
{txt}{space 8}dem08 {c |}{col 15}{res}{space 2}-.4946298{col 27}{space 2} .0483153{col 38}{space 1}  -10.24{col 47}{space 3}0.000{col 55}{space 4}-.5893261{col 68}{space 3}-.3999335
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-2.375053{col 27}{space 2} .2794076{col 38}{space 1}   -8.50{col 47}{space 3}0.000{col 55}{space 4}-2.922682{col 68}{space 3}-1.827424
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 53.7256278   41.5342302   12.1913976   .525664143    23.19
{txt}{col 17}        ATT {c |}{res} 53.7256278   48.5331051   5.19252267   .798524986     6.50
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .229677621  -.072486701   .302164322   .015818258    19.10
{txt}{col 17}        ATT {c |}{res} .229677621   .111461659   .118215962   .024489128     4.83
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

           {c |} psmatch2:
 psmatch2: {c |}   Common
 Treatment {c |}  support
assignment {c |} On suppor {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
 Untreated {c |}{res}     1,767 {txt}{c |}{res}     1,767 
{txt}   Treated {c |}{res}     1,278 {txt}{c |}{res}     1,278 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     3,045 {txt}{c |}{res}     3,045 
{txt}
{com}. drop iwps6_treated
{txt}
{com}. gen iwps6_treated = 1/_pscore if _treated == 1
{txt}(2,637 missing values generated)

{com}. recode iwps6_treated (.=0)
{txt}(iwps6_treated: 2637 changes made)

{com}. drop iwps6_untreated
{txt}
{com}. gen iwps6_untreated = 1/(1-_pscore) if _treated == 0
{txt}(2,148 missing values generated)

{com}. recode iwps6_untreated (. = 0)
{txt}(iwps6_untreated: 2148 changes made)

{com}. drop iwps6
{txt}
{com}. gen iwps6 = iwps6_treated + iwps6_untreated
{txt}
{com}. 
. // TP1 
. tabstat iwps1, by(tp1interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: iwps1
{col 6}by categories of: tp1interacted (Factionalism (1+))

{ralign 13:tp1interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}      906  3.370786  .7996344  3.224922  5.713379  2.190554
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2139  1.422852  .1330285  1.408344  1.833224   1.22369
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  2.002434  .9979302  1.474086  5.713379   1.22369
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct, by(tp1interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp1interacted (Factionalism (1+))

{ralign 13:tp1interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}      906   51.2489  31.57538     61.45      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2139   58.6689  30.02154      68.7      95.8      .098
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56.46117  30.67513      66.9      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income, by(tp1interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp1interacted (Factionalism (1+))

{ralign 13:tp1interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}      906  56582.74  16987.02   53221.5    125675     23773
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2139  57548.31  15636.32     53732    129821     31368
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  57261.02  16053.34     53562    129821     23773
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age, by(tp1interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp1interacted (Factionalism (1+))

{ralign 13:tp1interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}      906   37.2479  3.720759     37.45      51.1        26
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2139  37.82468  3.433841      37.6      55.7      28.2
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  37.65307  3.530899      37.6      55.7        26
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num, by(tp1interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp1interacted (Factionalism (1+))

{ralign 13:tp1interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}      906  3.415011  1.734905         3         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2139  3.432445  1.642369         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  3.427258   1.67017         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08, by(tp1interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp1interacted (Factionalism (1+))

{ralign 13:tp1interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}      906  .6633554  .4728232         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2139  .5507246  .4975367         1         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  .5842365  .4929341         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct [aw=iwps1], by(tp1interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp1interacted (Factionalism (1+))

{ralign 13:tp1interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}      906  56.92157  30.74155      67.3      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2139  56.56965  30.78127        67      95.8      .098
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56.74591  30.75485  67.24631      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income [aw=iwps1], by(tp1interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp1interacted (Factionalism (1+))

{ralign 13:tp1interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}      906  57211.84  16797.83     53746    125675     23773
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2139  57264.49  15619.08     53505    129821     31368
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  57238.12   16216.3  53702.81    129821     23773
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age [aw=iwps1], by(tp1interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp1interacted (Factionalism (1+))

{ralign 13:tp1interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}      906  37.70038   3.72951      38.1      51.1        26
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2139  37.66658  3.444086      37.5      55.7      28.2
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  37.68351  3.589053      37.7      55.7        26
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num [aw=iwps1], by(tp1interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp1interacted (Factionalism (1+))

{ralign 13:tp1interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}      906  3.417965  1.717341         3         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2139  3.427465  1.658384         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  3.422707  1.687781         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08 [aw=iwps1], by(tp1interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp1interacted (Factionalism (1+))

{ralign 13:tp1interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}      906  .5769618  .4943142         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2139  .5825329  .4932566         1         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  .5797426  .4936812         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. covbal tp1interacted white_pct median_income median_age density_num dem08


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  58.6689}}}{space 1}{space 1}{ralign 9:{res:{sf: 901.2926}}}{space 1}{space 1}{ralign 9:{res:{sf:-.8600945}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  51.2489}}}{space 1}{space 1}{ralign 9:{res:{sf: 997.0047}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2921334}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2408443}}}{space 1}{space 1}{ralign 9:{res:{sf: .9040004}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57548.31}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.44e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.176069}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56582.74}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.89e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.101024}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0591442}}}{space 1}{space 1}{ralign 9:{res:{sf: .8472947}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.82468}}}{space 1}{space 1}{ralign 9:{res:{sf: 11.79126}}}{space 1}{space 1}{ralign 9:{res:{sf: .4192505}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  37.2479}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.84405}}}{space 1}{space 1}{ralign 9:{res:{sf:-.1290813}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1611043}}}{space 1}{space 1}{ralign 9:{res:{sf: .8517208}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.432445}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.697375}}}{space 1}{space 1}{ralign 9:{res:{sf:-.1987157}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.415011}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.009896}}}{space 1}{space 1}{ralign 9:{res:{sf: .0782997}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0103204}}}{space 1}{space 1}{ralign 9:{res:{sf: .8961689}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5507246}}}{space 1}{space 1}{ralign 9:{res:{sf: .2475427}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2039508}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .6633554}}}{space 1}{space 1}{ralign 9:{res:{sf: .2235618}}}{space 1}{space 1}{ralign 9:{res:{sf:-.6913605}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: -.232067}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.107268}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. covbal tp1interacted white_pct median_income median_age density_num dem08, wt(iwps1) 


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56.56965}}}{space 1}{space 1}{ralign 9:{res:{sf: 947.4865}}}{space 1}{space 1}{ralign 9:{res:{sf:-.7400495}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56.92157}}}{space 1}{space 1}{ralign 9:{res:{sf: 945.0429}}}{space 1}{space 1}{ralign 9:{res:{sf:-.5765511}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0114402}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.002586}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57264.49}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.44e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.170841}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57211.84}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.82e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.068319}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0032457}}}{space 1}{space 1}{ralign 9:{res:{sf: .8645782}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.66658}}}{space 1}{space 1}{ralign 9:{res:{sf: 11.86173}}}{space 1}{space 1}{ralign 9:{res:{sf: .3826773}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.70038}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.90924}}}{space 1}{space 1}{ralign 9:{res:{sf:-.1374335}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0094162}}}{space 1}{space 1}{ralign 9:{res:{sf: .8527948}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.427465}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.750237}}}{space 1}{space 1}{ralign 9:{res:{sf:-.1813471}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.417965}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.949259}}}{space 1}{space 1}{ralign 9:{res:{sf: .0448972}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0056278}}}{space 1}{space 1}{ralign 9:{res:{sf: .9325178}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5825329}}}{space 1}{space 1}{ralign 9:{res:{sf: .2433021}}}{space 1}{space 1}{ralign 9:{res:{sf:-.3347232}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5769618}}}{space 1}{space 1}{ralign 9:{res:{sf: .2443466}}}{space 1}{space 1}{ralign 9:{res:{sf:-.3115602}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0112824}}}{space 1}{space 1}{ralign 9:{res:{sf: .9957253}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. 
. // TP2
. tabstat iwps2, by(tp2interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: iwps2
{col 6}by categories of: tp2interacted (Factionalism (2+))

{ralign 13:tp2interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1015  3.021802  .9920744  2.836626    8.5617  1.613622
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2030  1.497301  .2213413  1.450719  2.608316  1.157094
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  2.005468  .9365619  1.599737    8.5617  1.157094
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct, by(tp2interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp2interacted (Factionalism (2+))

{ralign 13:tp2interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1015  49.84013  31.38265      57.4      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2030   59.7717  29.77565      69.6      95.8      .098
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56.46117  30.67513      66.9      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income, by(tp2interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp2interacted (Factionalism (2+))

{ralign 13:tp2interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1015  55971.85  16572.38     52893    125675     23773
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2030  57905.61  15752.03     54034    129821     31368
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  57261.02  16053.34     53562    129821     23773
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age, by(tp2interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp2interacted (Factionalism (2+))

{ralign 13:tp2interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1015   36.9269  3.800765      37.1      51.1        26
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2030  38.01616  3.330001      37.8      55.7      28.2
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  37.65307  3.530899      37.6      55.7        26
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num, by(tp2interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp2interacted (Factionalism (2+))

{ralign 13:tp2interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1015  3.418719  1.793876         3         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2030  3.431527  1.605188         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  3.427258   1.67017         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08, by(tp2interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp2interacted (Factionalism (2+))

{ralign 13:tp2interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1015  .6738916  .4690186         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2030  .5394089  .4985673         1         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  .5842365  .4929341         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct [aw=iwps2], by(tp2interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp2interacted (Factionalism (2+))

{ralign 13:tp2interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1015  57.08499  30.54102      67.8      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2030  56.68899  30.84906  67.24631      95.8      .098
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56.88788  30.68909      67.4      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income [aw=iwps2], by(tp2interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp2interacted (Factionalism (2+))

{ralign 13:tp2interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1015  57293.65  16714.25     53746    125675     23773
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2030  57315.21  15619.04     53673    129821     31368
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  57304.38  16174.93     53732    129821     23773
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age [aw=iwps2], by(tp2interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp2interacted (Factionalism (2+))

{ralign 13:tp2interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1015  37.78757  3.842524      38.1      51.1        26
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2030  37.70127  3.334707      37.5      55.7      28.2
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  37.74462  3.598187      37.8      55.7        26
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num [aw=iwps2], by(tp2interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp2interacted (Factionalism (2+))

{ralign 13:tp2interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1015  3.416156  1.768195         3         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2030  3.431686  1.624307         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  3.423886  1.697751         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08 [aw=iwps2], by(tp2interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp2interacted (Factionalism (2+))

{ralign 13:tp2interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1015  .5749061  .4946009         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2030  .5817529  .4933927         1         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045   .578314  .4939099         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. covbal tp2interacted white_pct median_income median_age density_num dem08


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  59.7717}}}{space 1}{space 1}{ralign 9:{res:{sf: 886.5894}}}{space 1}{space 1}{ralign 9:{res:{sf:-.9368356}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 49.84013}}}{space 1}{space 1}{ralign 9:{res:{sf: 984.8707}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2284424}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3246702}}}{space 1}{space 1}{ralign 9:{res:{sf: .9002089}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57905.61}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.48e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.169382}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 55971.85}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.75e+08}}}{space 1}{space 1}{ralign 9:{res:{sf:  1.13695}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .1196084}}}{space 1}{space 1}{ralign 9:{res:{sf: .9034483}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 38.01616}}}{space 1}{space 1}{ralign 9:{res:{sf: 11.08891}}}{space 1}{space 1}{ralign 9:{res:{sf: .5430525}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  36.9269}}}{space 1}{space 1}{ralign 9:{res:{sf: 14.44581}}}{space 1}{space 1}{ralign 9:{res:{sf: -.090053}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3048467}}}{space 1}{space 1}{ralign 9:{res:{sf: .7676207}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.431527}}}{space 1}{space 1}{ralign 9:{res:{sf:  2.57663}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2282732}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.418719}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.217992}}}{space 1}{space 1}{ralign 9:{res:{sf: .0700078}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0075245}}}{space 1}{space 1}{ralign 9:{res:{sf: .8006949}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5394089}}}{space 1}{space 1}{ralign 9:{res:{sf: .2485694}}}{space 1}{space 1}{ralign 9:{res:{sf:-.1581274}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .6738916}}}{space 1}{space 1}{ralign 9:{res:{sf: .2199784}}}{space 1}{space 1}{ralign 9:{res:{sf:-.7418783}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.2778463}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.129972}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. covbal tp2interacted white_pct median_income median_age density_num dem08, wt(iwps2)   


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56.68899}}}{space 1}{space 1}{ralign 9:{res:{sf: 951.6643}}}{space 1}{space 1}{ralign 9:{res:{sf:-.7557939}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57.08499}}}{space 1}{space 1}{ralign 9:{res:{sf:  932.754}}}{space 1}{space 1}{ralign 9:{res:{sf:-.5915777}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0129008}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.020274}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57315.21}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.44e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.168488}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57293.65}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.79e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.086668}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0013326}}}{space 1}{space 1}{ralign 9:{res:{sf: .8732424}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.70127}}}{space 1}{space 1}{ralign 9:{res:{sf: 11.12027}}}{space 1}{space 1}{ralign 9:{res:{sf: .4570228}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.78757}}}{space 1}{space 1}{ralign 9:{res:{sf: 14.76499}}}{space 1}{space 1}{ralign 9:{res:{sf:-.0467259}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0239878}}}{space 1}{space 1}{ralign 9:{res:{sf: .7531512}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.431686}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.638375}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2110586}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.416156}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.126512}}}{space 1}{space 1}{ralign 9:{res:{sf: .0374522}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0091476}}}{space 1}{space 1}{ralign 9:{res:{sf: .8438715}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5817529}}}{space 1}{space 1}{ralign 9:{res:{sf: .2434364}}}{space 1}{space 1}{ralign 9:{res:{sf:-.3314725}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5749061}}}{space 1}{space 1}{ralign 9:{res:{sf: .2446301}}}{space 1}{space 1}{ralign 9:{res:{sf:-.3030444}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01386}}}{space 1}{space 1}{ralign 9:{res:{sf: .9951203}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. 
. // TP3 (Used in Main Analysis)
. tabstat iwps3, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: iwps3
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  2.601226  .9822676  2.287199  10.17146   1.30266
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  1.630837  .3178716  1.565331  4.232256  1.123941
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  2.008334  .8128893  1.778633  10.17146  1.123941
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  37.91121  33.62297      33.5      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  47.99778  36.30572  62.92498      95.8      .098
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  44.07394   35.6231      52.9      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  54554.61  16778.47     51647    125675     19311
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  54992.37  15243.99  51699.84    129821     25630
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  54822.07  15857.91   51666.1    129821     19311
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  36.59133  3.955628      36.7      51.1      22.3
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  37.92868  3.468013      37.8      55.7        21
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  37.40843  3.722482      37.4      55.7        21
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  3.443204  1.845518         3         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  3.408027  1.550996         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  3.421711  1.671606         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08, by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  .6822062  .4657718         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  .5242475  .4995161         1         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915   .585696  .4926644         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct [aw=iwps3], by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523   44.6073   35.0285        48      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  44.23373   36.3982      56.1      95.8      .098
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  44.42196  35.71015  53.24518      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income [aw=iwps3], by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  54583.93  16216.02     51738    125675     19311
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  54751.43  15365.18  51575.74    129821     25630
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  54667.03  15797.55     51669    129821     19311
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age [aw=iwps3], by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  37.59828  4.049905      37.7      51.1      22.3
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  37.48003  3.542402      37.4      55.7        21
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  37.53961   3.80647      37.5      55.7        21
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num [aw=iwps3], by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  3.415663  1.813842         3         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  3.421446  1.584961         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  3.418532  1.703879         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08 [aw=iwps3], by(tp3interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp3interacted (Factionalism (treatment))

{ralign 13:tp3interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1523  .5700364  .4952332         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     2392  .5804759  .4935843         1         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3915  .5752159  .4943733         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. covbal tp3interacted white_pct median_income median_age density_num dem08


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 47.99778}}}{space 1}{space 1}{ralign 9:{res:{sf: 1318.105}}}{space 1}{space 1}{ralign 9:{res:{sf:-.3183181}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.91121}}}{space 1}{space 1}{ralign 9:{res:{sf: 1130.504}}}{space 1}{space 1}{ralign 9:{res:{sf: .2273559}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .2882696}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.165945}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 54992.37}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.32e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.188313}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 54554.61}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.82e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.095125}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0273092}}}{space 1}{space 1}{ralign 9:{res:{sf: .8254529}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.92868}}}{space 1}{space 1}{ralign 9:{res:{sf: 12.02712}}}{space 1}{space 1}{ralign 9:{res:{sf: .2636573}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 36.59133}}}{space 1}{space 1}{ralign 9:{res:{sf: 15.64699}}}{space 1}{space 1}{ralign 9:{res:{sf:-.1317571}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3595191}}}{space 1}{space 1}{ralign 9:{res:{sf: .7686534}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.408027}}}{space 1}{space 1}{ralign 9:{res:{sf:  2.40559}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2629315}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.443204}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.405938}}}{space 1}{space 1}{ralign 9:{res:{sf: .0499716}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0206364}}}{space 1}{space 1}{ralign 9:{res:{sf:  .706293}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5242475}}}{space 1}{space 1}{ralign 9:{res:{sf: .2495164}}}{space 1}{space 1}{ralign 9:{res:{sf:-.0971042}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .6822062}}}{space 1}{space 1}{ralign 9:{res:{sf: .2169434}}}{space 1}{space 1}{ralign 9:{res:{sf:-.7826409}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.3270781}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.150145}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. covbal tp3interacted white_pct median_income median_age density_num dem08, wt(iwps3)   


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 44.23373}}}{space 1}{space 1}{ralign 9:{res:{sf: 1324.829}}}{space 1}{space 1}{ralign 9:{res:{sf:-.1404653}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  44.6073}}}{space 1}{space 1}{ralign 9:{res:{sf: 1226.996}}}{space 1}{space 1}{ralign 9:{res:{sf:-.0692032}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0104583}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.079734}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 54751.43}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.36e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.179253}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 54583.93}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.63e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.091498}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0106035}}}{space 1}{space 1}{ralign 9:{res:{sf: .8978149}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.48003}}}{space 1}{space 1}{ralign 9:{res:{sf: 12.54861}}}{space 1}{space 1}{ralign 9:{res:{sf: .0176654}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.59828}}}{space 1}{space 1}{ralign 9:{res:{sf: 16.40173}}}{space 1}{space 1}{ralign 9:{res:{sf:-.0089147}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0310801}}}{space 1}{space 1}{ralign 9:{res:{sf: .7650784}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.421446}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.512101}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2551532}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.415663}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.290021}}}{space 1}{space 1}{ralign 9:{res:{sf: .0525519}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0033953}}}{space 1}{space 1}{ralign 9:{res:{sf: .7635516}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5804759}}}{space 1}{space 1}{ralign 9:{res:{sf: .2436255}}}{space 1}{space 1}{ralign 9:{res:{sf: -.326156}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5700364}}}{space 1}{space 1}{ralign 9:{res:{sf: .2452559}}}{space 1}{space 1}{ralign 9:{res:{sf: -.282935}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0211151}}}{space 1}{space 1}{ralign 9:{res:{sf:  .993352}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. 
. // TP4
. tabstat iwps4, by(tp4interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: iwps4
{col 6}by categories of: tp4interacted (Factionalism (4+))

{ralign 13:tp4interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1370  2.270565  1.089708  1.915879  11.90029  1.196113
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1675  1.800932  .5297361  1.660269  4.889108   1.09706
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  2.012228  .8619587   1.74378  11.90029   1.09706
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct, by(tp4interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp4interacted (Factionalism (4+))

{ralign 13:tp4interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1370      48.6  30.10593  51.57548      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1675  62.89091  29.62898      72.8      95.8      .098
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56.46117  30.67513      66.9      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income, by(tp4interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp4interacted (Factionalism (4+))

{ralign 13:tp4interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1370  57353.12   16782.1     53969    125675     23773
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1675  57185.69  15436.37     53400    129821     31368
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  57261.02  16053.34     53562    129821     23773
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age, by(tp4interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp4interacted (Factionalism (4+))

{ralign 13:tp4interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1370  36.77314   3.66967      36.8      51.1        26
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1675  38.37278  3.241229      38.2      55.7      28.2
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  37.65307  3.530899      37.6      55.7        26
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num, by(tp4interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp4interacted (Factionalism (4+))

{ralign 13:tp4interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1370   3.39562  1.864586         3         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1675  3.453134  1.492528         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  3.427258   1.67017         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08, by(tp4interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp4interacted (Factionalism (4+))

{ralign 13:tp4interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1370  .6817518  .4659665         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1675  .5044776  .5001293         1         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  .5842365  .4929341         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct [aw=iwps4], by(tp4interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp4interacted (Factionalism (4+))

{ralign 13:tp4interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1370  57.16022  29.85714    65.512      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1675  56.61201  32.16464      68.7      95.8      .098
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56.89032  31.01071      67.6      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income [aw=iwps4], by(tp4interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp4interacted (Factionalism (4+))

{ralign 13:tp4interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1370  56771.82  15873.36     53617    125675     23773
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1675  57017.79  15823.54     53194    129821     31368
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56892.92  15846.66     53367    129821     23773
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age [aw=iwps4], by(tp4interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp4interacted (Factionalism (4+))

{ralign 13:tp4interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1370  37.95204  3.973447      38.1      51.1        26
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1675   37.8031  3.238375      37.6      55.7      28.2
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  37.87871  3.630335      37.8      55.7        26
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num [aw=iwps4], by(tp4interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp4interacted (Factionalism (4+))

{ralign 13:tp4interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1370  3.410142  1.822482         3         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1675  3.433083  1.546915         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  3.421436  1.692176         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08 [aw=iwps4], by(tp4interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp4interacted (Factionalism (4+))

{ralign 13:tp4interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1370   .560325  .4965288         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1675  .5740391  .4946355         1         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  .5670767  .4955617         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. covbal tp4interacted white_pct median_income median_age density_num dem08


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 62.89091}}}{space 1}{space 1}{ralign 9:{res:{sf: 877.8767}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.165571}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     48.6}}}{space 1}{space 1}{ralign 9:{res:{sf: 906.3672}}}{space 1}{space 1}{ralign 9:{res:{sf: -.204672}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4784622}}}{space 1}{space 1}{ralign 9:{res:{sf: .9685662}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57185.69}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.38e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.271926}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57353.12}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.82e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.014992}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0103844}}}{space 1}{space 1}{ralign 9:{res:{sf: .8460542}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 38.37278}}}{space 1}{space 1}{ralign 9:{res:{sf: 10.50557}}}{space 1}{space 1}{ralign 9:{res:{sf: .5909959}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 36.77314}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.46648}}}{space 1}{space 1}{ralign 9:{res:{sf:  .078339}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4620447}}}{space 1}{space 1}{ralign 9:{res:{sf: .7801271}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.453134}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.227641}}}{space 1}{space 1}{ralign 9:{res:{sf:-.3044808}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  3.39562}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.476679}}}{space 1}{space 1}{ralign 9:{res:{sf: .0376733}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0340554}}}{space 1}{space 1}{ralign 9:{res:{sf: .6407382}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5044776}}}{space 1}{space 1}{ralign 9:{res:{sf: .2501293}}}{space 1}{space 1}{ralign 9:{res:{sf:-.0179112}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .6817518}}}{space 1}{space 1}{ralign 9:{res:{sf: .2171248}}}{space 1}{space 1}{ralign 9:{res:{sf:-.7803917}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.3667617}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.152007}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. covbal tp4interacted white_pct median_income median_age density_num dem08, wt(iwps4)


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56.61201}}}{space 1}{space 1}{ralign 9:{res:{sf: 1034.564}}}{space 1}{space 1}{ralign 9:{res:{sf:-.7720092}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57.16022}}}{space 1}{space 1}{ralign 9:{res:{sf: 891.4488}}}{space 1}{space 1}{ralign 9:{res:{sf:-.5838715}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0176656}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.160542}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57017.79}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.50e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.237982}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56771.82}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.52e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.063059}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0155201}}}{space 1}{space 1}{ralign 9:{res:{sf: .9937325}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  37.8031}}}{space 1}{space 1}{ralign 9:{res:{sf: 10.48707}}}{space 1}{space 1}{ralign 9:{res:{sf: .3921272}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.95204}}}{space 1}{space 1}{ralign 9:{res:{sf: 15.78828}}}{space 1}{space 1}{ralign 9:{res:{sf: .2299587}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0410918}}}{space 1}{space 1}{ralign 9:{res:{sf: .6642314}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.433083}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.392946}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2557532}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.410142}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.321439}}}{space 1}{space 1}{ralign 9:{res:{sf: .0113943}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0135717}}}{space 1}{space 1}{ralign 9:{res:{sf: .7204545}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5740391}}}{space 1}{space 1}{ralign 9:{res:{sf: .2446643}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2994577}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .560325}}}{space 1}{space 1}{ralign 9:{res:{sf: .2465408}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2430758}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0276726}}}{space 1}{space 1}{ralign 9:{res:{sf: .9923884}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. 
. // TP5
. tabstat iwps5, by(tp5interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: iwps5
{col 6}by categories of: tp5interacted (Factionalism (5+))

{ralign 13:tp5interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1626  1.907205  .8090041   1.64216  9.374566  1.115858
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1419  2.114147  .7642422  1.876879  6.255573  1.108325
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  2.003642  .7950648  1.764152  9.374566  1.108325
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct, by(tp5interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp5interacted (Factionalism (5+))

{ralign 13:tp5interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1626  49.64615  29.87255      55.1      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1419  64.27035  29.71584  74.13611      95.8      .098
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56.46117  30.67513      66.9      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income, by(tp5interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp5interacted (Factionalism (5+))

{ralign 13:tp5interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1626  57867.12  17397.01     54202    129821     23773
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1419   56566.5  14334.56     53160    124627     31368
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  57261.02  16053.34     53562    129821     23773
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age, by(tp5interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp5interacted (Factionalism (5+))

{ralign 13:tp5interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1626  36.87595  3.688871      36.8      51.5        26
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1419  38.54355  3.111754      38.4      55.7      29.8
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  37.65307  3.530899      37.6      55.7        26
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num, by(tp5interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp5interacted (Factionalism (5+))

{ralign 13:tp5interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1626  3.389914  1.874984         3         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1419  3.470049  1.398538         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  3.427258   1.67017         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08, by(tp5interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp5interacted (Factionalism (5+))

{ralign 13:tp5interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1626  .6580566  .4745067         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1419  .4996476  .5001761         0         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  .5842365  .4929341         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct [aw=iwps5], by(tp5interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp5interacted (Factionalism (5+))

{ralign 13:tp5interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1626  56.90901  29.57757      65.3      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1419  56.46464  33.00975      69.5      95.8      .098
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56.69051  31.30779  67.78248      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income [aw=iwps5], by(tp5interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp5interacted (Factionalism (5+))

{ralign 13:tp5interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1626  56870.88  16376.62     53324    129821     23773
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1419  56905.62  14905.72     53512    124627     31368
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56887.96  15668.07     53400    129821     23773
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age [aw=iwps5], by(tp5interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp5interacted (Factionalism (5+))

{ralign 13:tp5interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1626  37.94269  4.051926      37.8      51.5        26
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1419  37.87369  3.095938      37.7      55.7      29.8
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  37.90876  3.613188      37.8      55.7        26
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num [aw=iwps5], by(tp5interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp5interacted (Factionalism (5+))

{ralign 13:tp5interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1626  3.437228  1.831079         4         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1419  3.441754  1.469727         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  3.439454  1.662973         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08 [aw=iwps5], by(tp5interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp5interacted (Factionalism (5+))

{ralign 13:tp5interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1626  .5665199  .4957078         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1419  .5708473  .4951297         1         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  .5686477  .4953464         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. covbal tp5interacted white_pct median_income median_age density_num dem08


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 64.27035}}}{space 1}{space 1}{ralign 9:{res:{sf: 883.0312}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.261134}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 49.64615}}}{space 1}{space 1}{ralign 9:{res:{sf: 892.3693}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2876162}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .490839}}}{space 1}{space 1}{ralign 9:{res:{sf: .9895357}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  56566.5}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.05e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.296758}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57867.12}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.03e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.018248}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0815972}}}{space 1}{space 1}{ralign 9:{res:{sf:  .678921}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 38.54355}}}{space 1}{space 1}{ralign 9:{res:{sf:  9.68301}}}{space 1}{space 1}{ralign 9:{res:{sf: .6432429}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 36.87595}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.60777}}}{space 1}{space 1}{ralign 9:{res:{sf: .1855496}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4886686}}}{space 1}{space 1}{ralign 9:{res:{sf: .7115794}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.470049}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.955908}}}{space 1}{space 1}{ralign 9:{res:{sf:-.3763778}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.389914}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.515566}}}{space 1}{space 1}{ralign 9:{res:{sf: .0206495}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0484493}}}{space 1}{space 1}{ralign 9:{res:{sf: .5563564}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4996476}}}{space 1}{space 1}{ralign 9:{res:{sf: .2501762}}}{space 1}{space 1}{ralign 9:{res:{sf: .0014094}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .6580566}}}{space 1}{space 1}{ralign 9:{res:{sf: .2251566}}}{space 1}{space 1}{ralign 9:{res:{sf:-.6663982}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.3249345}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.111121}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. covbal tp5interacted white_pct median_income median_age density_num dem08, wt(iwps5)


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56.46464}}}{space 1}{space 1}{ralign 9:{res:{sf: 1089.644}}}{space 1}{space 1}{ralign 9:{res:{sf:-.7667816}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56.90901}}}{space 1}{space 1}{ralign 9:{res:{sf: 874.8328}}}{space 1}{space 1}{ralign 9:{res:{sf:-.6090279}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0141786}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.245545}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56905.62}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.22e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.216741}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56870.88}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.68e+08}}}{space 1}{space 1}{ralign 9:{res:{sf:  1.09123}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0022189}}}{space 1}{space 1}{ralign 9:{res:{sf: .8284329}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.87369}}}{space 1}{space 1}{ralign 9:{res:{sf:  9.58483}}}{space 1}{space 1}{ralign 9:{res:{sf: .4143686}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.94269}}}{space 1}{space 1}{ralign 9:{res:{sf:  16.4181}}}{space 1}{space 1}{ralign 9:{res:{sf: .3546666}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0191343}}}{space 1}{space 1}{ralign 9:{res:{sf: .5837964}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.441754}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.160098}}}{space 1}{space 1}{ralign 9:{res:{sf:-.3332608}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.437228}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.352852}}}{space 1}{space 1}{ralign 9:{res:{sf:-.0343757}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0027262}}}{space 1}{space 1}{ralign 9:{res:{sf: .6442569}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5708473}}}{space 1}{space 1}{ralign 9:{res:{sf: .2451534}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2862776}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5665199}}}{space 1}{space 1}{ralign 9:{res:{sf: .2457262}}}{space 1}{space 1}{ralign 9:{res:{sf: -.268466}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0087348}}}{space 1}{space 1}{ralign 9:{res:{sf: .9976689}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. 
. // TP6
. tabstat iwps2, by(tp6interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: iwps2
{col 6}by categories of: tp6interacted (Factionalism (6+))

{ralign 13:tp6interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1767  2.411415  1.045495  2.092695    8.5617  1.157094
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1278  1.444194  .1895871   1.39812  2.324182  1.166426
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  2.005468  .9365619  1.599737    8.5617  1.157094
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct, by(tp6interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp6interacted (Factionalism (6+))

{ralign 13:tp6interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1767   50.0681  29.97631  56.24864      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1278  65.30042  29.42052   75.3152      95.8       .17
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56.46117  30.67513      66.9      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income, by(tp6interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp6interacted (Factionalism (6+))

{ralign 13:tp6interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1767  57583.26   17152.5     53970    129821     23773
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1278  56815.48  14390.76     53367    124627     31368
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  57261.02  16053.34     53562    129821     23773
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age, by(tp6interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp6interacted (Factionalism (6+))

{ralign 13:tp6interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1767  36.98364  3.685174      36.9      51.5        26
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1278  38.57864  3.075715      38.4      55.7      29.8
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  37.65307  3.530899      37.6      55.7        26
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num, by(tp6interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp6interacted (Factionalism (6+))

{ralign 13:tp6interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1767  3.369553  1.864869         3         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1278  3.507042  1.352348         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  3.427258   1.67017         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08, by(tp6interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp6interacted (Factionalism (6+))

{ralign 13:tp6interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1767   .672326  .4694981         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1278  .4624413  .4987825         0         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  .5842365  .4929341         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat white_pct [aw=iwps6], by(tp6interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: white_pct
{col 6}by categories of: tp6interacted (Factionalism (6+))

{ralign 13:tp6interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1767  56.77621  29.57478      65.3      96.6      .026
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1278  56.31802  33.50251   69.9644      95.8       .17
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56.55058  31.56522        68      96.6      .026
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_income [aw=iwps6], by(tp6interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_income
{col 6}by categories of: tp6interacted (Factionalism (6+))

{ralign 13:tp6interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1767  56915.15   16302.8     53336    129821     23773
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1278  56808.86   14854.1     53367    124627     31368
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  56862.81  15603.71     53348    129821     23773
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat median_age [aw=iwps6], by(tp6interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: median_age
{col 6}by categories of: tp6interacted (Factionalism (6+))

{ralign 13:tp6interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1767  37.89221  3.998394      37.7      51.5        26
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1278  37.88253  3.088843      37.8      55.7      29.8
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  37.88744   3.57894      37.8      55.7        26
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat density_num [aw=iwps6], by(tp6interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: density_num
{col 6}by categories of: tp6interacted (Factionalism (6+))

{ralign 13:tp6interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1767  3.432304  1.825476         4         6         1
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1278  3.430492  1.434868         4         6         1
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  3.431412  1.644501         4         6         1
{txt}{hline 14}{c BT}{hline 60}

{com}. tabstat dem08 [aw=iwps6], by(tp6interacted) statistics(count mean sd median max min) columns(statistics)

{txt}Summary for variables: dem08
{col 6}by categories of: tp6interacted (Factionalism (6+))

{ralign 13:tp6interacted} {...}
{c |}         N      mean        sd       p50       max       min
{hline 14}{c +}{hline 60}
{ralign 13:0} {...}
{c |}{...}
 {res}     1767  .5691775  .4953315         1         1         0
{txt}{ralign 13:1} {...}
{c |}{...}
 {res}     1278  .5697201  .4953091         1         1         0
{txt}{hline 14}{c +}{hline 60}
{ralign 13:Total} {...}
{c |}{...}
 {res}     3045  .5694447  .4952353         1         1         0
{txt}{hline 14}{c BT}{hline 60}

{com}. covbal tp6interacted white_pct median_income median_age density_num dem08


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 65.30042}}}{space 1}{space 1}{ralign 9:{res:{sf: 865.5672}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.349121}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  50.0681}}}{space 1}{space 1}{ralign 9:{res:{sf: 898.5789}}}{space 1}{space 1}{ralign 9:{res:{sf:-.3144986}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5128776}}}{space 1}{space 1}{ralign 9:{res:{sf: .9632624}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56815.48}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.07e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.279177}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 57583.26}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.94e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.054587}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0484952}}}{space 1}{space 1}{ralign 9:{res:{sf: .7039021}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 38.57864}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.460021}}}{space 1}{space 1}{ralign 9:{res:{sf: .6673004}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 36.98364}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.58051}}}{space 1}{space 1}{ralign 9:{res:{sf: .1960719}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4699242}}}{space 1}{space 1}{ralign 9:{res:{sf: .6965881}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.507042}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.828846}}}{space 1}{space 1}{ralign 9:{res:{sf:-.4368236}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.369553}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.477736}}}{space 1}{space 1}{ralign 9:{res:{sf: .0315096}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0844066}}}{space 1}{space 1}{ralign 9:{res:{sf: .5258726}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4624413}}}{space 1}{space 1}{ralign 9:{res:{sf:  .248784}}}{space 1}{space 1}{ralign 9:{res:{sf: .1506604}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .672326}}}{space 1}{space 1}{ralign 9:{res:{sf: .2204285}}}{space 1}{space 1}{ralign 9:{res:{sf:-.7342937}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.4333222}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.128638}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. covbal tp6interacted white_pct median_income median_age density_num dem08, wt(iwps6)


{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treated}{space 1}{c |}{res}{txt}{space 1}{rcenter 31:Control}{space 1}{c |}{space 1}{rcenter 20:Balance}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Mean}{space 1}{space 1}{ralign 9:Variance}{space 1}{space 1}{ralign 9:Skewness}{space 1}{c |}{space 1}{ralign 9:Std-diff}{space 1}{space 1}{ralign 9:Var-ratio}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:white_pct}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56.31802}}}{space 1}{space 1}{ralign 9:{res:{sf: 1122.418}}}{space 1}{space 1}{ralign 9:{res:{sf:-.7585686}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56.77621}}}{space 1}{space 1}{ralign 9:{res:{sf: 874.6677}}}{space 1}{space 1}{ralign 9:{res:{sf:-.6176795}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0144997}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.283251}}}{space 1}
{space 0}{space 0}{ralign 12:median_inc~e}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56808.86}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.21e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.171708}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 56915.15}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.66e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.109833}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0068155}}}{space 1}{space 1}{ralign 9:{res:{sf: .8301715}}}{space 1}
{space 0}{space 0}{ralign 12:median_age}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.88253}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.540953}}}{space 1}{space 1}{ralign 9:{res:{sf: .3626705}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 37.89221}}}{space 1}{space 1}{ralign 9:{res:{sf: 15.98715}}}{space 1}{space 1}{ralign 9:{res:{sf: .3445134}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0027105}}}{space 1}{space 1}{ralign 9:{res:{sf: .5967888}}}{space 1}
{space 0}{space 0}{ralign 12:density_num}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.430492}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.058847}}}{space 1}{space 1}{ralign 9:{res:{sf:-.3599021}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.432304}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.332362}}}{space 1}{space 1}{ralign 9:{res:{sf:-.0414601}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-.0011032}}}{space 1}{space 1}{ralign 9:{res:{sf: .6178342}}}{space 1}
{space 0}{space 0}{ralign 12:dem08}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5697201}}}{space 1}{space 1}{ralign 9:{res:{sf: .2453311}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2816319}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .5691775}}}{space 1}{space 1}{ralign 9:{res:{sf: .2453533}}}{space 1}{space 1}{ralign 9:{res:{sf:-.2793971}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .0010954}}}{space 1}{space 1}{ralign 9:{res:{sf: .9999093}}}{space 1}
{space 0}{hline 13}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}{hline 11}{c  BT}{hline 11}{hline 11}


{res}{txt}
{com}. 
. *******************Robustness Checks********************************************
. 
. /// Moving TP Groups Boundary ///
> 
. // Pres Vote Share
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps1], t(tp1interacted) p(posttreatment) cl(geoid) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}130{col 28}129{txt}{col 40}259
   Treated:{res}{col 13}305{col 28}306{txt}{col 40}611
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.585{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}47.195{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}4.610{col 27}{txt}{c |} {res}1.620{col 37}{txt}{c |} {res}2.85{col 47}{txt}{c |} {res}0.005***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.759{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}47.798{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}5.040{col 27}{txt}{c |} {res}1.903{col 37}{txt}{c |} {res}2.65{col 47}{txt}{c |} {res}0.008***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.429{col 27}{txt}{c |} {res}1.041{col 37}{txt}{c |} {res}0.41{col 47}{txt}{c |} {res}0.680
{txt}{hline 56}
R-square:{res}    0.02
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps2], t(tp2interacted) p(posttreatment) cl(geoid) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}145{col 28}145{txt}{col 40}290
   Treated:{res}{col 13}290{col 28}290{txt}{col 40}580
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.279{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}47.775{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}5.496{col 27}{txt}{c |} {res}1.545{col 37}{txt}{c |} {res}3.56{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.344{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}48.752{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}6.408{col 27}{txt}{c |} {res}1.832{col 37}{txt}{c |} {res}3.50{col 47}{txt}{c |} {res}0.001***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.912{col 27}{txt}{c |} {res}1.132{col 37}{txt}{c |} {res}0.81{col 47}{txt}{c |} {res}0.421
{txt}{hline 56}
R-square:{res}    0.04
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps4], t(tp4interacted) p(posttreatment) cl(geoid) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}198{col 28}194{txt}{col 40}392
   Treated:{res}{col 13}237{col 28}241{txt}{col 40}478
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}43.076{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}48.874{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}5.798{col 27}{txt}{c |} {res}1.414{col 37}{txt}{c |} {res}4.10{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.567{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}50.663{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}8.097{col 27}{txt}{c |} {res}1.722{col 37}{txt}{c |} {res}4.70{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}2.298{col 27}{txt}{c |} {res}1.219{col 37}{txt}{c |} {res}1.89{col 47}{txt}{c |} {res}0.060*
{txt}{hline 56}
R-square:{res}    0.05
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps5], t(tp5interacted) p(posttreatment) cl(geoid) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}234{col 28}231{txt}{col 40}465
   Treated:{res}{col 13}201{col 28}204{txt}{col 40}405
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}43.367{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}49.264{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}5.897{col 27}{txt}{c |} {res}1.356{col 37}{txt}{c |} {res}4.35{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}43.067{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}51.748{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}8.681{col 27}{txt}{c |} {res}1.637{col 37}{txt}{c |} {res}5.30{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}2.784{col 27}{txt}{c |} {res}1.154{col 37}{txt}{c |} {res}2.41{col 47}{txt}{c |} {res}0.016**
{txt}{hline 56}
R-square:{res}    0.06
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps6], t(tp6interacted) p(posttreatment) cl(geoid) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}256{col 28}249{txt}{col 40}505
   Treated:{res}{col 13}179{col 28}186{txt}{col 40}365
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}43.653{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}49.798{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}6.145{col 27}{txt}{c |} {res}1.365{col 37}{txt}{c |} {res}4.50{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}43.377{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}52.264{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}8.887{col 27}{txt}{c |} {res}1.619{col 37}{txt}{c |} {res}5.49{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}2.742{col 27}{txt}{c |} {res}1.010{col 37}{txt}{c |} {res}2.71{col 47}{txt}{c |} {res}0.007***
{txt}{hline 56}
R-square:{res}    0.07
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. // Leg Position
. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps1], t(tp1interacted) p(posttreatment) cl(geoid) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}130{col 28}129{txt}{col 40}259
   Treated:{res}{col 13}305{col 28}306{txt}{col 40}611
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.041{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.001{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.041{col 27}{txt}{c |} {res}0.047{col 37}{txt}{c |} {res}0.87{col 47}{txt}{c |} {res}0.386
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.010{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.138{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.128{col 27}{txt}{c |} {res}0.050{col 37}{txt}{c |} {res}2.58{col 47}{txt}{c |} {res}0.010**
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.087{col 27}{txt}{c |} {res}0.043{col 37}{txt}{c |} {res}2.01{col 47}{txt}{c |} {res}0.045**
{txt}{hline 56}
R-square:{res}    0.02
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps2], t(tp2interacted) p(posttreatment) cl(geoid) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}145{col 28}145{txt}{col 40}290
   Treated:{res}{col 13}290{col 28}290{txt}{col 40}580
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.046{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.008{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.054{col 27}{txt}{c |} {res}0.046{col 37}{txt}{c |} {res}1.18{col 47}{txt}{c |} {res}0.241
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.005{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.161{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.166{col 27}{txt}{c |} {res}0.048{col 37}{txt}{c |} {res}3.42{col 47}{txt}{c |} {res}0.001***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.112{col 27}{txt}{c |} {res}0.044{col 37}{txt}{c |} {res}2.55{col 47}{txt}{c |} {res}0.011**
{txt}{hline 56}
R-square:{res}    0.03
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps4], t(tp4interacted) p(posttreatment) cl(geoid) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}198{col 28}194{txt}{col 40}392
   Treated:{res}{col 13}237{col 28}241{txt}{col 40}478
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.045{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.029{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.074{col 27}{txt}{c |} {res}0.044{col 37}{txt}{c |} {res}1.69{col 47}{txt}{c |} {res}0.092*
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.012{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.211{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.223{col 27}{txt}{c |} {res}0.047{col 37}{txt}{c |} {res}4.73{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.149{col 27}{txt}{c |} {res}0.043{col 37}{txt}{c |} {res}3.44{col 47}{txt}{c |} {res}0.001***
{txt}{hline 56}
R-square:{res}    0.05
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps5], t(tp5interacted) p(posttreatment) cl(geoid) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}234{col 28}231{txt}{col 40}465
   Treated:{res}{col 13}201{col 28}204{txt}{col 40}405
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.040{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.042{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.082{col 27}{txt}{c |} {res}0.044{col 37}{txt}{c |} {res}1.88{col 47}{txt}{c |} {res}0.061*
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.001{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.242{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.240{col 27}{txt}{c |} {res}0.047{col 37}{txt}{c |} {res}5.10{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.158{col 27}{txt}{c |} {res}0.046{col 37}{txt}{c |} {res}3.44{col 47}{txt}{c |} {res}0.001***
{txt}{hline 56}
R-square:{res}    0.06
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps6], t(tp6interacted) p(posttreatment) cl(geoid) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}256{col 28}249{txt}{col 40}505
   Treated:{res}{col 13}179{col 28}186{txt}{col 40}365
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.030{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.048{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.077{col 27}{txt}{c |} {res}0.044{col 37}{txt}{c |} {res}1.74{col 47}{txt}{c |} {res}0.082*
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.018{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.249{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.231{col 27}{txt}{c |} {res}0.048{col 37}{txt}{c |} {res}4.79{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.153{col 27}{txt}{c |} {res}0.046{col 37}{txt}{c |} {res}3.32{col 47}{txt}{c |} {res}0.001***
{txt}{hline 56}
R-square:{res}    0.05
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Effect just using TP Group Presence ///
> 
. psmatch2 tp_treatment3 white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,045
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    479.04
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1327.5274{txt}{col 49}Pseudo R2{col 67}= {res}    0.1528

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp_treatment3{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}white_pct {c |}{col 15}{res}{space 2} .0099647{col 27}{space 2} .0009958{col 38}{space 1}   10.01{col 47}{space 3}0.000{col 55}{space 4} .0080129{col 68}{space 3} .0119165
{txt}median_income {c |}{col 15}{res}{space 2}-9.80e-06{col 27}{space 2} 1.74e-06{col 38}{space 1}   -5.62{col 47}{space 3}0.000{col 55}{space 4}-.0000132{col 68}{space 3}-6.38e-06
{txt}{space 3}median_age {c |}{col 15}{res}{space 2} .1025953{col 27}{space 2} .0093788{col 38}{space 1}   10.94{col 47}{space 3}0.000{col 55}{space 4} .0842132{col 68}{space 3} .1209774
{txt}{space 2}density_num {c |}{col 15}{res}{space 2}-.0209679{col 27}{space 2} .0156205{col 38}{space 1}   -1.34{col 47}{space 3}0.179{col 55}{space 4}-.0515835{col 68}{space 3} .0096477
{txt}{space 8}dem08 {c |}{col 15}{res}{space 2}-.4691373{col 27}{space 2} .0593445{col 38}{space 1}   -7.91{col 47}{space 3}0.000{col 55}{space 4}-.5854504{col 68}{space 3}-.3528243
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-2.564051{col 27}{space 2} .3234163{col 38}{space 1}   -7.93{col 47}{space 3}0.000{col 55}{space 4}-3.197936{col 68}{space 3}-1.930167
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 50.4505315   32.4013405    18.049191   .607834121    29.69
{txt}{col 17}        ATT {c |}{res} 50.3616726   39.9953304   10.3663422   1.48643628     6.97
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .132434692  -.238578783   .371013475   .019115941    19.41
{txt}{col 17}        ATT {c |}{res} .127639441  -.072497672   .200137114   .037646309     5.32
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0        641 {txt}{c |}{res}       641 
{txt}   Treated {c |}{res}        41      2,363 {txt}{c |}{res}     2,404 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}        41      3,004 {txt}{c |}{res}     3,045 
{txt}
{com}. drop iwps3_tpgroups_treated
{txt}
{com}. gen iwps3_tpgroups_treated = 1/_pscore if _treated == 1
{txt}(1,511 missing values generated)

{com}. recode iwps3_tpgroups_treated (.=0)
{txt}(iwps3_tpgroups_treated: 1511 changes made)

{com}. drop iwps3_tpgroups_untreated
{txt}
{com}. gen iwps3_tpgroups_untreated = 1/(1-_pscore) if _treated == 0
{txt}(3,274 missing values generated)

{com}. recode iwps3_tpgroups_untreated (. = 0)
{txt}(iwps3_tpgroups_untreated: 3274 changes made)

{com}. drop iwps3_tpgroups
{txt}
{com}. gen iwps3_tpgroups = iwps3_tpgroups_treated + iwps3_tpgroups_untreated
{txt}
{com}. 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3_tpgroups], t(tp_treatment3) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}91{col 28}92{txt}{col 40}183
   Treated:{res}{col 13}344{col 28}343{txt}{col 40}687
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}38.553{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}47.443{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}8.891{col 27}{txt}{c |} {res}2.350{col 37}{txt}{c |} {res}3.78{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}34.581{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}49.112{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}14.531{col 27}{txt}{c |} {res}1.913{col 37}{txt}{c |} {res}7.60{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}5.641{col 27}{txt}{c |} {res}1.946{col 37}{txt}{c |} {res}2.90{col 47}{txt}{c |} {res}0.004***
{txt}{hline 56}
R-square:{res}    0.15
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3_tpgroups], t(tp_treatment3) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}91{col 28}92{txt}{col 40}183
   Treated:{res}{col 13}344{col 28}343{txt}{col 40}687
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.074{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.005{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.079{col 27}{txt}{c |} {res}0.065{col 37}{txt}{c |} {res}1.21{col 47}{txt}{c |} {res}0.226
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.114{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.156{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.270{col 27}{txt}{c |} {res}0.075{col 37}{txt}{c |} {res}3.59{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.191{col 27}{txt}{c |} {res}0.065{col 37}{txt}{c |} {res}2.94{col 47}{txt}{c |} {res}0.003***
{txt}{hline 56}
R-square:{res}    0.06
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Effect just using TP Primaries ///
> 
. psmatch2 tp_candidate1014treat white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,915
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}     41.84
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2289.8337{txt}{col 49}Pseudo R2{col 67}= {res}    0.0091

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp_candidate1014treat{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}white_pct {c |}{col 23}{res}{space 2} .0010339{col 35}{space 2} .0006717{col 46}{space 1}    1.54{col 55}{space 3}0.124{col 63}{space 4}-.0002827{col 76}{space 3} .0023504
{txt}{space 8}median_income {c |}{col 23}{res}{space 2} 9.27e-07{col 35}{space 2} 1.43e-06{col 46}{space 1}    0.65{col 55}{space 3}0.516{col 63}{space 4}-1.87e-06{col 76}{space 3} 3.73e-06
{txt}{space 11}median_age {c |}{col 23}{res}{space 2} .0136247{col 35}{space 2} .0061517{col 46}{space 1}    2.21{col 55}{space 3}0.027{col 63}{space 4} .0015676{col 76}{space 3} .0256819
{txt}{space 10}density_num {c |}{col 23}{res}{space 2} .0119429{col 35}{space 2} .0128618{col 46}{space 1}    0.93{col 55}{space 3}0.353{col 63}{space 4}-.0132657{col 76}{space 3} .0371516
{txt}{space 16}dem08 {c |}{col 23}{res}{space 2}-.2117937{col 35}{space 2} .0442652{col 46}{space 1}   -4.78{col 55}{space 3}0.000{col 63}{space 4}-.2985518{col 76}{space 3}-.1250355
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} .0740513{col 35}{space 2} .2297846{col 46}{space 1}    0.32{col 55}{space 3}0.747{col 63}{space 4}-.3763183{col 76}{space 3} .5244208
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 49.1549299   41.9556549   7.19927494   .533599143    13.49
{txt}{col 17}        ATT {c |}{res} 49.1560451   44.5080207   4.64802439   .792508474     5.86
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .089416254  -.043679263   .133095517   .015968626     8.33
{txt}{col 17}        ATT {c |}{res} .089550018   .021831389   .067718628   .021063674     3.21
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0      1,085 {txt}{c |}{res}     1,085 
{txt}   Treated {c |}{res}         1      2,829 {txt}{c |}{res}     2,830 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}         1      3,914 {txt}{c |}{res}     3,915 
{txt}
{com}. drop iwps3_tpprim_treated
{txt}
{com}. gen iwps3_tpprim_treated = 1/_pscore if _treated == 1
{txt}(1,085 missing values generated)

{com}. recode iwps3_tpprim_treated (.=0)
{txt}(iwps3_tpprim_treated: 1085 changes made)

{com}. drop iwps3_tpprim_untreated
{txt}
{com}. gen iwps3_tpprim_untreated = 1/(1-_pscore) if _treated == 0
{txt}(2,830 missing values generated)

{com}. recode iwps3_tpprim_untreated (. = 0)
{txt}(iwps3_tpprim_untreated: 2830 changes made)

{com}. drop iwps3_tpprim
{txt}
{com}. gen iwps3_tpprim = iwps3_tpprim_treated + iwps3_tpprim_untreated
{txt}
{com}. 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3_tpprim], t(tp_candidate1014treat) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}121{col 28}120{txt}{col 40}241
   Treated:{res}{col 13}314{col 28}315{txt}{col 40}629
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.089{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}47.008{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}4.919{col 27}{txt}{c |} {res}1.648{col 37}{txt}{c |} {res}2.99{col 47}{txt}{c |} {res}0.003***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.414{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}47.467{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}5.053{col 27}{txt}{c |} {res}1.913{col 37}{txt}{c |} {res}2.64{col 47}{txt}{c |} {res}0.009***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.134{col 27}{txt}{c |} {res}0.763{col 37}{txt}{c |} {res}0.18{col 47}{txt}{c |} {res}0.861
{txt}{hline 56}
R-square:{res}    0.02
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3_tpprim], t(tp_candidate1014treat) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}121{col 28}120{txt}{col 40}241
   Treated:{res}{col 13}314{col 28}315{txt}{col 40}629
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.041{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}-0.005{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.036{col 27}{txt}{c |} {res}0.048{col 37}{txt}{c |} {res}0.76{col 47}{txt}{c |} {res}0.450
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.004{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.132{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.128{col 27}{txt}{c |} {res}0.050{col 37}{txt}{c |} {res}2.57{col 47}{txt}{c |} {res}0.010**
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.092{col 27}{txt}{c |} {res}0.042{col 37}{txt}{c |} {res}2.19{col 47}{txt}{c |} {res}0.029**
{txt}{hline 56}
R-square:{res}    0.02
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Robustness Check Requiring >25% Primary Vote Share ///
> 
. psmatch2 tp3interactedthresh white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,045
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    133.91
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res} -2033.487{txt}{col 49}Pseudo R2{col 67}= {res}    0.0319

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp3interactedthresh{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}white_pct {c |}{col 21}{res}{space 2} .0045196{col 33}{space 2} .0008491{col 44}{space 1}    5.32{col 53}{space 3}0.000{col 61}{space 4} .0028554{col 74}{space 3} .0061838
{txt}{space 6}median_income {c |}{col 21}{res}{space 2}-3.61e-06{col 33}{space 2} 1.48e-06{col 44}{space 1}   -2.44{col 53}{space 3}0.015{col 61}{space 4}-6.51e-06{col 74}{space 3}-7.06e-07
{txt}{space 9}median_age {c |}{col 21}{res}{space 2} .0464235{col 33}{space 2} .0074994{col 44}{space 1}    6.19{col 53}{space 3}0.000{col 61}{space 4}  .031725{col 74}{space 3} .0611221
{txt}{space 8}density_num {c |}{col 21}{res}{space 2}-.0285685{col 33}{space 2} .0139573{col 44}{space 1}   -2.05{col 53}{space 3}0.041{col 61}{space 4}-.0559243{col 74}{space 3}-.0012127
{txt}{space 14}dem08 {c |}{col 21}{res}{space 2}-.1167629{col 33}{space 2} .0473762{col 44}{space 1}   -2.46{col 53}{space 3}0.014{col 61}{space 4}-.2096185{col 74}{space 3}-.0239072
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.525237{col 33}{space 2} .2678205{col 44}{space 1}   -5.69{col 53}{space 3}0.000{col 61}{space 4}-2.050156{col 74}{space 3}-1.000319
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 51.0611641   41.4553646    9.6057995   .537205965    17.88
{txt}{col 17}        ATT {c |}{res} 51.0345537    44.964823   6.06973067   .797373253     7.61
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .150629022  -.059114449   .209743472   .016136163    13.00
{txt}{col 17}        ATT {c |}{res} .149716981   .017948265   .131768716    .02226295     5.92
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0      1,398 {txt}{c |}{res}     1,398 
{txt}   Treated {c |}{res}         4      1,643 {txt}{c |}{res}     1,647 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}         4      3,041 {txt}{c |}{res}     3,045 
{txt}
{com}. drop iwps3_thresh_treated
{txt}
{com}. gen iwps3_thresh_treated = 1/_pscore if _treated == 1
{txt}(2,268 missing values generated)

{com}. recode iwps3_thresh_treated (.=0)
{txt}(iwps3_thresh_treated: 2268 changes made)

{com}. drop iwps3_thresh_untreated
{txt}
{com}. gen iwps3_thresh_untreated = 1/(1-_pscore) if _treated == 0
{txt}(2,517 missing values generated)

{com}. recode iwps3_thresh_untreated (. = 0)
{txt}(iwps3_thresh_untreated: 2517 changes made)

{com}. drop iwps3_thresh
{txt}
{com}. gen iwps3_thresh = iwps3_thresh_treated + iwps3_thresh_untreated
{txt}
{com}. 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3], t(tp3interactedthresh) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}202{col 28}198{txt}{col 40}400
   Treated:{res}{col 13}233{col 28}237{txt}{col 40}470
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}43.213{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}48.293{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}5.080{col 27}{txt}{c |} {res}1.413{col 37}{txt}{c |} {res}3.60{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.731{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}49.765{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}7.034{col 27}{txt}{c |} {res}1.705{col 37}{txt}{c |} {res}4.13{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}1.953{col 27}{txt}{c |} {res}1.178{col 37}{txt}{c |} {res}1.66{col 47}{txt}{c |} {res}0.098*
{txt}{hline 56}
R-square:{res}    0.04
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3], t(tp3interactedthresh) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}202{col 28}198{txt}{col 40}400
   Treated:{res}{col 13}233{col 28}237{txt}{col 40}470
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.025{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.001{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.026{col 27}{txt}{c |} {res}0.043{col 37}{txt}{c |} {res}0.61{col 47}{txt}{c |} {res}0.543
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.007{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.187{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.180{col 27}{txt}{c |} {res}0.047{col 37}{txt}{c |} {res}3.86{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.154{col 27}{txt}{c |} {res}0.044{col 37}{txt}{c |} {res}3.53{col 47}{txt}{c |} {res}0.000***
{txt}{hline 56}
R-square:{res}    0.03
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Robustness Check Requiring >0$ Raised ///
> 
. psmatch2 interactedtreatmentreceipts white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,045
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    218.71
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1973.1824{txt}{col 49}Pseudo R2{col 67}= {res}    0.0525

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}interactedtreatmentreceipts{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}white_pct {c |}{col 29}{res}{space 2} .0054005{col 41}{space 2} .0008546{col 52}{space 1}    6.32{col 61}{space 3}0.000{col 69}{space 4} .0037254{col 82}{space 3} .0070756
{txt}{space 14}median_income {c |}{col 29}{res}{space 2}-3.67e-06{col 41}{space 2} 1.50e-06{col 52}{space 1}   -2.44{col 61}{space 3}0.015{col 69}{space 4}-6.61e-06{col 82}{space 3}-7.25e-07
{txt}{space 17}median_age {c |}{col 29}{res}{space 2} .0480669{col 41}{space 2} .0075987{col 52}{space 1}    6.33{col 61}{space 3}0.000{col 69}{space 4} .0331737{col 82}{space 3}   .06296
{txt}{space 16}density_num {c |}{col 29}{res}{space 2} .0205232{col 41}{space 2} .0140208{col 52}{space 1}    1.46{col 61}{space 3}0.143{col 69}{space 4}-.0069571{col 82}{space 3} .0480034
{txt}{space 22}dem08 {c |}{col 29}{res}{space 2}-.3498052{col 41}{space 2} .0480632{col 52}{space 1}   -7.28{col 61}{space 3}0.000{col 69}{space 4}-.4440074{col 82}{space 3} -.255603
{txt}{space 22}_cons {c |}{col 29}{res}{space 2}-1.592167{col 41}{space 2} .2711446{col 52}{space 1}   -5.87{col 61}{space 3}0.000{col 69}{space 4}-2.123601{col 82}{space 3}-1.060734
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 51.7032222    40.013272   11.6899502   .527075882    22.18
{txt}{col 17}        ATT {c |}{res} 51.7032222   44.6550777   7.04814446   .811691814     8.68
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .171573742  -.099701368    .27127511   .015935106    17.02
{txt}{col 17}        ATT {c |}{res} .171573742   .014448814   .157124928   .023020461     6.83
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

           {c |} psmatch2:
 psmatch2: {c |}   Common
 Treatment {c |}  support
assignment {c |} On suppor {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
 Untreated {c |}{res}     1,316 {txt}{c |}{res}     1,316 
{txt}   Treated {c |}{res}     1,729 {txt}{c |}{res}     1,729 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     3,045 {txt}{c |}{res}     3,045 
{txt}
{com}. drop iwps3_receipts_treated
{txt}
{com}. gen iwps3_receipts_treated = 1/_pscore if _treated == 1
{txt}(2,186 missing values generated)

{com}. recode iwps3_receipts_treated (.=0)
{txt}(iwps3_receipts_treated: 2186 changes made)

{com}. drop iwps3_receipts_untreated
{txt}
{com}. gen iwps3_receipts_untreated = 1/(1-_pscore) if _treated == 0
{txt}(2,599 missing values generated)

{com}. recode iwps3_receipts_untreated (. = 0)
{txt}(iwps3_receipts_untreated: 2599 changes made)

{com}. drop iwps3_receipts
{txt}
{com}. gen iwps3_receipts = iwps3_receipts_treated + iwps3_receipts_untreated
{txt}
{com}. 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3_receipts], t(interactedtreatmentreceipts) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}192{col 28}185{txt}{col 40}377
   Treated:{res}{col 13}243{col 28}250{txt}{col 40}493
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.744{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}49.036{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}6.292{col 27}{txt}{c |} {res}1.392{col 37}{txt}{c |} {res}4.52{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.425{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}50.240{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}7.815{col 27}{txt}{c |} {res}1.698{col 37}{txt}{c |} {res}4.60{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}1.523{col 27}{txt}{c |} {res}1.106{col 37}{txt}{c |} {res}1.38{col 47}{txt}{c |} {res}0.169
{txt}{hline 56}
R-square:{res}    0.05
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3_receipts], t(interactedtreatmentreceipts) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}192{col 28}185{txt}{col 40}377
   Treated:{res}{col 13}243{col 28}250{txt}{col 40}493
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.041{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.026{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.067{col 27}{txt}{c |} {res}0.044{col 37}{txt}{c |} {res}1.54{col 47}{txt}{c |} {res}0.124
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.008{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.200{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.208{col 27}{txt}{c |} {res}0.046{col 37}{txt}{c |} {res}4.48{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.141{col 27}{txt}{c |} {res}0.044{col 37}{txt}{c |} {res}3.22{col 47}{txt}{c |} {res}0.001***
{txt}{hline 56}
R-square:{res}    0.04
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Robustness Check Only 'Quality' Candidates ///
> 
. psmatch2 tp3interactedquality white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,045
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    281.58
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1583.2284{txt}{col 49}Pseudo R2{col 67}= {res}    0.0817

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp3interactedquality{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}white_pct {c |}{col 22}{res}{space 2} .0046427{col 34}{space 2} .0009616{col 45}{space 1}    4.83{col 54}{space 3}0.000{col 62}{space 4}  .002758{col 75}{space 3} .0065274
{txt}{space 7}median_income {c |}{col 22}{res}{space 2}-7.39e-06{col 34}{space 2} 1.74e-06{col 45}{space 1}   -4.24{col 54}{space 3}0.000{col 62}{space 4}-.0000108{col 75}{space 3}-3.97e-06
{txt}{space 10}median_age {c |}{col 22}{res}{space 2} .0160654{col 34}{space 2} .0081999{col 45}{space 1}    1.96{col 54}{space 3}0.050{col 62}{space 4}-6.06e-06{col 75}{space 3} .0321369
{txt}{space 9}density_num {c |}{col 22}{res}{space 2}-.0167896{col 34}{space 2} .0160857{col 45}{space 1}   -1.04{col 54}{space 3}0.297{col 62}{space 4} -.048317{col 75}{space 3} .0147379
{txt}{space 15}dem08 {c |}{col 22}{res}{space 2}-.7330284{col 34}{space 2} .0517783{col 45}{space 1}  -14.16{col 54}{space 3}0.000{col 62}{space 4} -.834512{col 75}{space 3}-.6315447
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-.6825905{col 34}{space 2}  .297953{col 45}{space 1}   -2.29{col 54}{space 3}0.022{col 62}{space 4}-1.266568{col 75}{space 3}-.0986134
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 57.3199785   43.0274147   14.2925638   .592732352    24.11
{txt}{col 17}        ATT {c |}{res} 57.3199785   48.9187902   8.40118835   .771287729    10.89
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .381375648  -.056743511   .438119158   .017249448    25.40
{txt}{col 17}        ATT {c |}{res} .381375648   .140435233   .240940415   .025155409     9.58
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

           {c |} psmatch2:
 psmatch2: {c |}   Common
 Treatment {c |}  support
assignment {c |} On suppor {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
 Untreated {c |}{res}     2,273 {txt}{c |}{res}     2,273 
{txt}   Treated {c |}{res}       772 {txt}{c |}{res}       772 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     3,045 {txt}{c |}{res}     3,045 
{txt}
{com}. drop iwps3_quality_treated
{txt}
{com}. gen iwps3_quality_treated = 1/_pscore if _treated == 1
{txt}(3,143 missing values generated)

{com}. recode iwps3_quality_treated (.=0)
{txt}(iwps3_quality_treated: 3143 changes made)

{com}. drop iwps3_quality_untreated
{txt}
{com}. gen iwps3_quality_untreated = 1/(1-_pscore) if _treated == 0
{txt}(1,642 missing values generated)

{com}. recode iwps3_quality_untreated (. = 0)
{txt}(iwps3_quality_untreated: 1642 changes made)

{com}. drop iwps3_quality
{txt}
{com}. gen iwps3_quality = iwps3_quality_treated + iwps3_quality_untreated
{txt}
{com}. 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3_quality], t(tp3interactedquality) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}327{col 28}323{txt}{col 40}650
   Treated:{res}{col 13}108{col 28}112{txt}{col 40}220
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}43.802{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}54.049{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}10.246{col 27}{txt}{c |} {res}1.327{col 37}{txt}{c |} {res}7.72{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}43.895{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}55.529{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}11.634{col 27}{txt}{c |} {res}1.449{col 37}{txt}{c |} {res}8.03{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}1.387{col 27}{txt}{c |} {res}0.969{col 37}{txt}{c |} {res}1.43{col 47}{txt}{c |} {res}0.153
{txt}{hline 56}
R-square:{res}    0.15
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share (Quality Only)) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3_quality], t(tp3interactedquality) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}327{col 28}323{txt}{col 40}650
   Treated:{res}{col 13}108{col 28}112{txt}{col 40}220
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.039{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.101{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.140{col 27}{txt}{c |} {res}0.049{col 37}{txt}{c |} {res}2.85{col 47}{txt}{c |} {res}0.005***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.013{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.424{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.411{col 27}{txt}{c |} {res}0.052{col 37}{txt}{c |} {res}7.97{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.272{col 27}{txt}{c |} {res}0.058{col 37}{txt}{c |} {res}4.65{col 47}{txt}{c |} {res}0.000***
{txt}{hline 56}
R-square:{res}    0.16
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position (Quality Only)) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Robustness Check 12-14 Primaries (Counter to 2010 Wave Election Narrative) ///
> 
. psmatch2 interactedcandidate1214 white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,045
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    164.52
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2021.9982{txt}{col 49}Pseudo R2{col 67}= {res}    0.0391

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}interactedcandidate1214{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}white_pct {c |}{col 25}{res}{space 2} .0049151{col 37}{space 2} .0008554{col 48}{space 1}    5.75{col 57}{space 3}0.000{col 65}{space 4} .0032385{col 78}{space 3} .0065917
{txt}{space 10}median_income {c |}{col 25}{res}{space 2}-2.70e-06{col 37}{space 2} 1.49e-06{col 48}{space 1}   -1.81{col 57}{space 3}0.071{col 65}{space 4}-5.63e-06{col 78}{space 3} 2.28e-07
{txt}{space 13}median_age {c |}{col 25}{res}{space 2} .0166889{col 37}{space 2} .0074012{col 48}{space 1}    2.25{col 57}{space 3}0.024{col 65}{space 4} .0021827{col 78}{space 3} .0311951
{txt}{space 12}density_num {c |}{col 25}{res}{space 2}-.0271619{col 37}{space 2} .0141054{col 48}{space 1}   -1.93{col 57}{space 3}0.054{col 65}{space 4} -.054808{col 78}{space 3} .0004843
{txt}{space 18}dem08 {c |}{col 25}{res}{space 2}-.4229032{col 37}{space 2} .0473082{col 48}{space 1}   -8.94{col 57}{space 3}0.000{col 65}{space 4}-.5156255{col 78}{space 3}-.3301808
{txt}{space 18}_cons {c |}{col 25}{res}{space 2}-.4970024{col 37}{space 2} .2649333{col 48}{space 1}   -1.88{col 57}{space 3}0.061{col 65}{space 4}-1.016262{col 78}{space 3} .0222574
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 52.3196235   41.6713098   10.6483138   .529953075    20.09
{txt}{col 17}        ATT {c |}{res} 52.3196235   46.6807736   5.63884995   .780205001     7.23
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .189662219  -.064549661    .25421188   .015903002    15.99
{txt}{col 17}        ATT {c |}{res} .189662219   .083209972   .106452247   .022994368     4.63
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

           {c |} psmatch2:
 psmatch2: {c |}   Common
 Treatment {c |}  support
assignment {c |} On suppor {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
 Untreated {c |}{res}     1,621 {txt}{c |}{res}     1,621 
{txt}   Treated {c |}{res}     1,424 {txt}{c |}{res}     1,424 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     3,045 {txt}{c |}{res}     3,045 
{txt}
{com}. drop iwps3_1214_treated
{txt}
{com}. gen iwps3_1214_treated = 1/_pscore if _treated == 1
{txt}(2,491 missing values generated)

{com}. recode iwps3_1214_treated (.=0)
{txt}(iwps3_1214_treated: 2491 changes made)

{com}. drop iwps3_1214_untreated
{txt}
{com}. gen iwps3_1214_untreated = 1/(1-_pscore) if _treated == 0
{txt}(2,294 missing values generated)

{com}. recode iwps3_1214_untreated (. = 0)
{txt}(iwps3_1214_untreated: 2294 changes made)

{com}. drop iwps3_1214
{txt}
{com}. gen iwps3_1214 = iwps3_1214_treated + iwps3_1214_untreated
{txt}
{com}. 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3_1214], t(interactedcandidate1214) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}239{col 28}226{txt}{col 40}465
   Treated:{res}{col 13}196{col 28}209{txt}{col 40}405
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}43.338{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}49.399{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}6.061{col 27}{txt}{c |} {res}1.313{col 37}{txt}{c |} {res}4.62{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.993{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}50.429{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}7.436{col 27}{txt}{c |} {res}1.565{col 37}{txt}{c |} {res}4.75{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}1.375{col 27}{txt}{c |} {res}0.825{col 37}{txt}{c |} {res}1.67{col 47}{txt}{c |} {res}0.096*
{txt}{hline 56}
R-square:{res}    0.05
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3_1214], t(interactedcandidate1214) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}239{col 28}226{txt}{col 40}465
   Treated:{res}{col 13}196{col 28}209{txt}{col 40}405
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.038{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.032{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.070{col 27}{txt}{c |} {res}0.043{col 37}{txt}{c |} {res}1.64{col 47}{txt}{c |} {res}0.102
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.015{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.202{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.186{col 27}{txt}{c |} {res}0.046{col 37}{txt}{c |} {res}4.09{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.116{col 27}{txt}{c |} {res}0.043{col 37}{txt}{c |} {res}2.72{col 47}{txt}{c |} {res}0.007***
{txt}{hline 56}
R-square:{res}    0.04
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Using Fixed Weights Based on 2008 District Boundaries ///
> 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = weight2008], t(tp3interacted) p(posttreatment) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 789{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}129{col 28}135{txt}{col 40}264
   Treated:{res}{col 13}264{col 28}261{txt}{col 40}525
{col 13}393{col 28}396
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}44.769{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}48.947{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}4.178{col 27}{txt}{c |} {res}1.484{col 37}{txt}{c |} {res}2.82{col 47}{txt}{c |} {res}0.005***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}43.140{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}50.993{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}7.853{col 27}{txt}{c |} {res}1.481{col 37}{txt}{c |} {res}5.30{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}3.676{col 27}{txt}{c |} {res}2.097{col 37}{txt}{c |} {res}1.75{col 47}{txt}{c |} {res}0.080*
{txt}{hline 56}
R-square:{res}    0.04
{txt}* Means and Standard Errors are estimated by linear regression
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = weight2008], t(tp3interacted) p(posttreatment) cl(geoid)

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 789{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}129{col 28}135{txt}{col 40}264
   Treated:{res}{col 13}264{col 28}261{txt}{col 40}525
{col 13}393{col 28}396
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.067{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.066{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.133{col 27}{txt}{c |} {res}0.055{col 37}{txt}{c |} {res}2.39{col 47}{txt}{c |} {res}0.017**
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.014{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.201{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.187{col 27}{txt}{c |} {res}0.057{col 37}{txt}{c |} {res}3.31{col 47}{txt}{c |} {res}0.001***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.054{col 27}{txt}{c |} {res}0.054{col 37}{txt}{c |} {res}1.01{col 47}{txt}{c |} {res}0.315
{txt}{hline 56}
R-square:{res}    0.05
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Robustness Check Using All TP Candidates ///
> 
. psmatch2 tpcandidateanyinteracted white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,045
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    313.16
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1768.8886{txt}{col 49}Pseudo R2{col 67}= {res}    0.0813

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tpcandidateanyinteracted{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}white_pct {c |}{col 26}{res}{space 2} .0069715{col 38}{space 2} .0008846{col 49}{space 1}    7.88{col 58}{space 3}0.000{col 66}{space 4} .0052377{col 79}{space 3} .0087053
{txt}{space 11}median_income {c |}{col 26}{res}{space 2}-5.88e-06{col 38}{space 2} 1.55e-06{col 49}{space 1}   -3.80{col 58}{space 3}0.000{col 66}{space 4}-8.91e-06{col 79}{space 3}-2.84e-06
{txt}{space 14}median_age {c |}{col 26}{res}{space 2} .0600803{col 38}{space 2} .0079817{col 49}{space 1}    7.53{col 58}{space 3}0.000{col 66}{space 4} .0444364{col 79}{space 3} .0757242
{txt}{space 13}density_num {c |}{col 26}{res}{space 2}-.0084184{col 38}{space 2} .0143585{col 49}{space 1}   -0.59{col 58}{space 3}0.558{col 66}{space 4}-.0365605{col 79}{space 3} .0197237
{txt}{space 19}dem08 {c |}{col 26}{res}{space 2}-.4289979{col 38}{space 2} .0508818{col 49}{space 1}   -8.43{col 58}{space 3}0.000{col 66}{space 4}-.5287244{col 79}{space 3}-.3292714
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-1.554618{col 38}{space 2} .2824168{col 49}{space 1}   -5.50{col 58}{space 3}0.000{col 66}{space 4}-2.108145{col 79}{space 3}-1.001091
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 51.3523801   36.9936436   14.3587365   .540249726    26.58
{txt}{col 17}        ATT {c |}{res} 51.3523801   43.5790234   7.77335671   .930898686     8.35
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .160291504  -.163322969   .323614473   .016598208    19.50
{txt}{col 17}        ATT {c |}{res} .160291504   .013164551   .147126953    .02453568     6.00
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

           {c |} psmatch2:
 psmatch2: {c |}   Common
 Treatment {c |}  support
assignment {c |} On suppor {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
 Untreated {c |}{res}       997 {txt}{c |}{res}       997 
{txt}   Treated {c |}{res}     2,048 {txt}{c |}{res}     2,048 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}     3,045 {txt}{c |}{res}     3,045 
{txt}
{com}. drop iwps3_alltp_treated
{txt}
{com}. gen iwps3_alltp_treated = 1/_pscore if _treated == 1
{txt}(1,867 missing values generated)

{com}. recode iwps3_alltp_treated (.=0)
{txt}(iwps3_alltp_treated: 1867 changes made)

{com}. drop iwps3_alltp_untreated
{txt}
{com}. gen iwps3_alltp_untreated = 1/(1-_pscore) if _treated == 0
{txt}(2,918 missing values generated)

{com}. recode iwps3_alltp_untreated (. = 0)
{txt}(iwps3_alltp_untreated: 2918 changes made)

{com}. drop iwps3_alltp
{txt}
{com}. gen iwps3_alltp = iwps3_alltp_treated + iwps3_alltp_untreated
{txt}
{com}. 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3_alltp], t(tpcandidateanyinteracted) p(posttreatment) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}143{col 28}142{txt}{col 40}285
   Treated:{res}{col 13}292{col 28}293{txt}{col 40}585
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}41.647{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}48.433{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}6.786{col 27}{txt}{c |} {res}1.464{col 37}{txt}{c |} {res}4.64{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}41.248{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}49.877{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}8.629{col 27}{txt}{c |} {res}1.473{col 37}{txt}{c |} {res}5.86{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}1.843{col 27}{txt}{c |} {res}2.077{col 37}{txt}{c |} {res}0.89{col 47}{txt}{c |} {res}0.375
{txt}{hline 56}
R-square:{res}    0.06
{txt}* Means and Standard Errors are estimated by linear regression
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share - IWPT) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace 
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3_alltp], t(tpcandidateanyinteracted) p(posttreatment) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}143{col 28}142{txt}{col 40}285
   Treated:{res}{col 13}292{col 28}293{txt}{col 40}585
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.055{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.019{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.074{col 27}{txt}{c |} {res}0.041{col 37}{txt}{c |} {res}1.80{col 47}{txt}{c |} {res}0.072*
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.043{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.185{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.228{col 27}{txt}{c |} {res}0.041{col 37}{txt}{c |} {res}5.51{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.154{col 27}{txt}{c |} {res}0.058{col 37}{txt}{c |} {res}2.64{col 47}{txt}{c |} {res}0.008***
{txt}{hline 56}
R-square:{res}    0.05
{txt}* Means and Standard Errors are estimated by linear regression
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position - IWPT) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Robustness Check Including 2016 'Tea Party' Primaries ///
> 
. psmatch2 tp3interacted16 white_pct median_income median_age density_num dem08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,045
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    304.84
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1812.3158{txt}{col 49}Pseudo R2{col 67}= {res}    0.0776

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp3interacted16{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}white_pct {c |}{col 17}{res}{space 2}  .006169{col 29}{space 2} .0008775{col 40}{space 1}    7.03{col 49}{space 3}0.000{col 57}{space 4} .0044491{col 70}{space 3}  .007889
{txt}{space 2}median_income {c |}{col 17}{res}{space 2}-4.75e-06{col 29}{space 2} 1.54e-06{col 40}{space 1}   -3.09{col 49}{space 3}0.002{col 57}{space 4}-7.77e-06{col 70}{space 3}-1.73e-06
{txt}{space 5}median_age {c |}{col 17}{res}{space 2} .0619839{col 29}{space 2} .0079363{col 40}{space 1}    7.81{col 49}{space 3}0.000{col 57}{space 4}  .046429{col 70}{space 3} .0775388
{txt}{space 4}density_num {c |}{col 17}{res}{space 2}-.0258717{col 29}{space 2} .0143076{col 40}{space 1}   -1.81{col 49}{space 3}0.071{col 57}{space 4}-.0539141{col 70}{space 3} .0021708
{txt}{space 10}dem08 {c |}{col 17}{res}{space 2} -.446902{col 29}{space 2} .0503446{col 40}{space 1}   -8.88{col 49}{space 3}0.000{col 57}{space 4}-.5455755{col 70}{space 3}-.3482285
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.632678{col 29}{space 2} .2807888{col 40}{space 1}   -5.81{col 49}{space 3}0.000{col 57}{space 4}-2.183014{col 70}{space 3}-1.082342
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 51.1538965   38.1574246   12.9964719   .542448534    23.96
{txt}{col 17}        ATT {c |}{res} 51.1376075    44.820731   6.31687651   .948025977     6.66
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .154134673  -.133918483   .288053157   .016557376    17.40
{txt}{col 17}        ATT {c |}{res} .153456697   .033808661   .119648036   .025462185     4.70
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0      1,055 {txt}{c |}{res}     1,055 
{txt}   Treated {c |}{res}         4      1,986 {txt}{c |}{res}     1,990 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}         4      3,041 {txt}{c |}{res}     3,045 
{txt}
{com}. drop iwps3_16_treated
{txt}
{com}. gen iwps3_16_treated = 1/_pscore if _treated == 1
{txt}(1,925 missing values generated)

{com}. recode iwps3_16_treated (.=0)
{txt}(iwps3_16_treated: 1925 changes made)

{com}. drop iwps3_16_untreated
{txt}
{com}. gen iwps3_16_untreated = 1/(1-_pscore) if _treated == 0
{txt}(2,860 missing values generated)

{com}. recode iwps3_16_untreated (. = 0)
{txt}(iwps3_16_untreated: 2860 changes made)

{com}. drop iwps3_16
{txt}
{com}. gen iwps3_16 = iwps3_16_treated + iwps3_16_untreated
{txt}
{com}. 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3_16], t(tp3interacted16) p(posttreatment) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}153{col 28}149{txt}{col 40}302
   Treated:{res}{col 13}282{col 28}286{txt}{col 40}568
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.602{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}48.168{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}5.566{col 27}{txt}{c |} {res}1.463{col 37}{txt}{c |} {res}3.81{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}41.686{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}49.613{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}7.927{col 27}{txt}{c |} {res}1.486{col 37}{txt}{c |} {res}5.33{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}2.361{col 27}{txt}{c |} {res}2.085{col 37}{txt}{c |} {res}1.13{col 47}{txt}{c |} {res}0.258
{txt}{hline 56}
R-square:{res}    0.05
{txt}* Means and Standard Errors are estimated by linear regression
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share - Inc 2016 Primaries) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons replace dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3_16], t(tp3interacted16) p(posttreatment) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}153{col 28}149{txt}{col 40}302
   Treated:{res}{col 13}282{col 28}286{txt}{col 40}568
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.028{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.013{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.041{col 27}{txt}{c |} {res}0.042{col 37}{txt}{c |} {res}0.97{col 47}{txt}{c |} {res}0.331
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.019{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.178{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.197{col 27}{txt}{c |} {res}0.043{col 37}{txt}{c |} {res}4.60{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.156{col 27}{txt}{c |} {res}0.060{col 37}{txt}{c |} {res}2.59{col 47}{txt}{c |} {res}0.010***
{txt}{hline 56}
R-square:{res}    0.03
{txt}* Means and Standard Errors are estimated by linear regression
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position - Inc 2016 Primaries) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// H2 2018 Post (Trump not on Ballot) ///
> 
. diff district_nokkenpoole if year ==2008 | year==2018 [aweight = iwps3], t(tp3interacted) p(posttreatment) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.046{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.021{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.067{col 27}{txt}{c |} {res}0.042{col 37}{txt}{c |} {res}1.58{col 47}{txt}{c |} {res}0.113
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.062{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.109{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.171{col 27}{txt}{c |} {res}0.042{col 37}{txt}{c |} {res}4.05{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.105{col 27}{txt}{c |} {res}0.060{col 37}{txt}{c |} {res}1.75{col 47}{txt}{c |} {res}0.080*
{txt}{hline 56}
R-square:{res}    0.02
{txt}* Means and Standard Errors are estimated by linear regression
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Adding Representative Replacement as a Further Control ///
> 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cov(repchangedummy1 repchangedummy2 repchangedummy3plus) cl(geoid) report
{res}{title:DIFFERENCE-IN-DIFFERENCES WITH COVARIATES}

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{res}{p}Report - Covariates and coefficients:{p_end}
{txt}{hline 21}{c -}{hline 12}{c -}{hline 11}{c -}{hline 9}{c -}{hline 10}
 Variable(s)         {c |}   Coeff.   {c |} Std. Err. {c |}    t    {c |}  P>|t|
{hline 21}{c +}{hline 12}{c +}{hline 11}{c +}{hline 9}{c +}{hline 10}
{res}repchanged~1{col 22}{txt}{c |} {res}2.675{col 35}{txt}{c |} {res}1.779{col 47}{txt}{c |} {res}1.504{col 57}{txt}{c |} {res}0.133
repchanged~2{col 22}{txt}{c |} {res}3.866{col 35}{txt}{c |} {res}2.073{col 47}{txt}{c |} {res}1.865{col 57}{txt}{c |} {res}0.063
repchanged~s{col 22}{txt}{c |} {res}1.169{col 35}{txt}{c |} {res}3.235{col 47}{txt}{c |} {res}0.361{col 57}{txt}{c |} {res}0.718
{txt}{hline 21}{c -}{hline 12}{c -}{hline 11}{c -}{hline 9}{c -}{hline 10}
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}41.446{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}46.742{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}5.296{col 27}{txt}{c |} {res}1.450{col 37}{txt}{c |} {res}3.65{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}40.748{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}48.110{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}7.362{col 27}{txt}{c |} {res}1.760{col 37}{txt}{c |} {res}4.18{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}2.066{col 27}{txt}{c |} {res}1.186{col 37}{txt}{c |} {res}1.74{col 47}{txt}{c |} {res}0.082*
{txt}{hline 56}
R-square:{res}    0.06
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cov(repchangedummy1 repchangedummy2 repchangedummy3plus) cl(geoid) report
{res}{title:DIFFERENCE-IN-DIFFERENCES WITH COVARIATES}

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{res}{p}Report - Covariates and coefficients:{p_end}
{txt}{hline 21}{c -}{hline 12}{c -}{hline 11}{c -}{hline 9}{c -}{hline 10}
 Variable(s)         {c |}   Coeff.   {c |} Std. Err. {c |}    t    {c |}  P>|t|
{hline 21}{c +}{hline 12}{c +}{hline 11}{c +}{hline 9}{c +}{hline 10}
{res}repchanged~1{col 22}{txt}{c |} {res}0.016{col 35}{txt}{c |} {res}0.045{col 47}{txt}{c |} {res}0.350{col 57}{txt}{c |} {res}0.726
repchanged~2{col 22}{txt}{c |} {res}-0.010{col 35}{txt}{c |} {res}0.062{col 47}{txt}{c |} {res}-0.155{col 57}{txt}{c |} {res}0.877
repchanged~s{col 22}{txt}{c |} {res}-0.071{col 35}{txt}{c |} {res}0.081{col 47}{txt}{c |} {res}-0.873{col 57}{txt}{c |} {res}0.383
{txt}{hline 21}{c -}{hline 12}{c -}{hline 11}{c -}{hline 9}{c -}{hline 10}
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.048{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.018{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.066{col 27}{txt}{c |} {res}0.044{col 37}{txt}{c |} {res}1.50{col 47}{txt}{c |} {res}0.134
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.014{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.181{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.195{col 27}{txt}{c |} {res}0.047{col 37}{txt}{c |} {res}4.12{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.129{col 27}{txt}{c |} {res}0.043{col 37}{txt}{c |} {res}2.96{col 47}{txt}{c |} {res}0.003***
{txt}{hline 56}
R-square:{res}    0.04
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Including Lagged DVs as controls ///
> 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cov(lagged_pres_vote_share) cl(geoid) 
{res}{title:DIFFERENCE-IN-DIFFERENCES WITH COVARIATES}

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.810{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.779{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}-0.031{col 27}{txt}{c |} {res}1.046{col 37}{txt}{c |} {res}-0.03{col 47}{txt}{c |} {res}0.976
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}3.489{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}4.794{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}1.306{col 27}{txt}{c |} {res}0.696{col 37}{txt}{c |} {res}1.88{col 47}{txt}{c |} {res}0.061*
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}1.337{col 27}{txt}{c |} {res}1.102{col 37}{txt}{c |} {res}1.21{col 47}{txt}{c |} {res}0.226
{txt}{hline 56}
R-square:{res}    0.73
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cov(lagged_nokkenpoole) cl(geoid) 
{res}{title:DIFFERENCE-IN-DIFFERENCES WITH COVARIATES}

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.014{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}-0.022{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}-0.008{col 27}{txt}{c |} {res}0.018{col 37}{txt}{c |} {res}-0.46{col 47}{txt}{c |} {res}0.649
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.024{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.015{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.039{col 27}{txt}{c |} {res}0.015{col 37}{txt}{c |} {res}2.55{col 47}{txt}{c |} {res}0.011**
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.047{col 27}{txt}{c |} {res}0.023{col 37}{txt}{c |} {res}2.06{col 47}{txt}{c |} {res}0.040**
{txt}{hline 56}
R-square:{res}    0.88
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Including alternative DV as controls ///
> 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cov(district_nokkenpoole) cl(geoid)
{res}{title:DIFFERENCE-IN-DIFFERENCES WITH COVARIATES}

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}43.737{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}47.870{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}4.133{col 27}{txt}{c |} {res}1.240{col 37}{txt}{c |} {res}3.33{col 47}{txt}{c |} {res}0.001***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.235{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}45.361{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}3.126{col 27}{txt}{c |} {res}1.171{col 37}{txt}{c |} {res}2.67{col 47}{txt}{c |} {res}0.008***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}-1.007{col 27}{txt}{c |} {res}1.141{col 37}{txt}{c |} {res}0.88{col 47}{txt}{c |} {res}0.378
{txt}{hline 56}
R-square:{res}    0.50
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cov(recent_presidential_share) cl(geoid)
{res}{title:DIFFERENCE-IN-DIFFERENCES WITH COVARIATES}

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.890{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}-0.937{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}-0.047{col 27}{txt}{c |} {res}0.038{col 37}{txt}{c |} {res}-1.24{col 47}{txt}{c |} {res}0.216
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.841{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}-0.802{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.039{col 27}{txt}{c |} {res}0.032{col 37}{txt}{c |} {res}1.23{col 47}{txt}{c |} {res}0.218
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.086{col 27}{txt}{c |} {res}0.038{col 37}{txt}{c |} {res}2.25{col 47}{txt}{c |} {res}0.025**
{txt}{hline 56}
R-square:{res}    0.50
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Leg Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Doubly Robust Estimators Comparison (Identical) ///
> 
. drdid recent_presidential_share if year ==2008 | year==2016 [iweight = iwps3], time(year) treatment(tp3interacted) cluster(geoid) all
{res}
{txt}Doubly robust difference-in-differences estimator summary
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATET         {txt}{c |}
{space 7}dripw {c |}{col 14}{res}{space 2} 2.031821{col 26}{space 2} 1.178669{col 37}{space 1}    1.72{col 46}{space 3}0.085{col 54}{space 4}-.2783284{col 67}{space 3}  4.34197
{txt}{space 3}dripw_rc1 {c |}{col 14}{res}{space 2} 2.031821{col 26}{space 2} 1.178669{col 37}{space 1}    1.72{col 46}{space 3}0.085{col 54}{space 4}-.2783284{col 67}{space 3}  4.34197
{txt}{space 7}drimp {c |}{col 14}{res}{space 2} 2.031821{col 26}{space 2} 1.178669{col 37}{space 1}    1.72{col 46}{space 3}0.085{col 54}{space 4}-.2783284{col 67}{space 3}  4.34197
{txt}{space 3}drimp_rc1 {c |}{col 14}{res}{space 2} 2.031821{col 26}{space 2} 1.178669{col 37}{space 1}    1.72{col 46}{space 3}0.085{col 54}{space 4}-.2783284{col 67}{space 3}  4.34197
{txt}{space 9}reg {c |}{col 14}{res}{space 2} 2.031821{col 26}{space 2} 1.178669{col 37}{space 1}    1.72{col 46}{space 3}0.085{col 54}{space 4}-.2783284{col 67}{space 3}  4.34197
{txt}{space 9}ipw {c |}{col 14}{res}{space 2} 2.308906{col 26}{space 2} 2.800775{col 37}{space 1}    0.82{col 46}{space 3}0.410{col 54}{space 4}-3.180513{col 67}{space 3} 7.798325
{txt}{space 6}stdipw {c |}{col 14}{res}{space 2} 2.031821{col 26}{space 2} 1.178669{col 37}{space 1}    1.72{col 46}{space 3}0.085{col 54}{space 4}-.2783284{col 67}{space 3}  4.34197
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p}Note: This table is provided for comparison across estimations only. You cannot use it to compare estimates across different estimators{p_end}
{cmd:dripw} :Doubly Robust IPW
{cmd:drimp} :Doubly Robust Improved estimator
{cmd:reg}   :Outcome regression or Regression augmented estimator
{cmd:ipw}   :Abadie(2005) IPW estimator
{cmd:stdipw}:Standardized IPW estimator
{cmd:sipwra}:IPW and Regression adjustment estimator.

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. drdid district_nokkenpoole if year ==2008 | year==2016 [iweight = iwps3], time(year) treatment(tp3interacted) cluster(geoid) all
{res}
{txt}Doubly robust difference-in-differences estimator summary
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATET         {txt}{c |}
{space 7}dripw {c |}{col 14}{res}{space 2} .1263497{col 26}{space 2}  .043348{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4} .0413891{col 67}{space 3} .2113102
{txt}{space 3}dripw_rc1 {c |}{col 14}{res}{space 2} .1263497{col 26}{space 2}  .043348{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4} .0413891{col 67}{space 3} .2113102
{txt}{space 7}drimp {c |}{col 14}{res}{space 2} .1263497{col 26}{space 2}  .043348{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4} .0413891{col 67}{space 3} .2113102
{txt}{space 3}drimp_rc1 {c |}{col 14}{res}{space 2} .1263497{col 26}{space 2}  .043348{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4} .0413891{col 67}{space 3} .2113102
{txt}{space 9}reg {c |}{col 14}{res}{space 2} .1263497{col 26}{space 2}  .043348{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4} .0413891{col 67}{space 3} .2113102
{txt}{space 9}ipw {c |}{col 14}{res}{space 2} .1273016{col 26}{space 2} .0431893{col 37}{space 1}    2.95{col 46}{space 3}0.003{col 54}{space 4} .0426521{col 67}{space 3}  .211951
{txt}{space 6}stdipw {c |}{col 14}{res}{space 2} .1263497{col 26}{space 2}  .043348{col 37}{space 1}    2.91{col 46}{space 3}0.004{col 54}{space 4} .0413891{col 67}{space 3} .2113102
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p}Note: This table is provided for comparison across estimations only. You cannot use it to compare estimates across different estimators{p_end}
{cmd:dripw} :Doubly Robust IPW
{cmd:drimp} :Doubly Robust Improved estimator
{cmd:reg}   :Outcome regression or Regression augmented estimator
{cmd:ipw}   :Abadie(2005) IPW estimator
{cmd:stdipw}:Standardized IPW estimator
{cmd:sipwra}:IPW and Regression adjustment estimator.

{com}. outreg2 using table_diff, ctitle(Legislator Position) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// More Granular Controls for Partisanship ///
> 
. // Including Pretreatment Partisan Index and 2008 PVI as Controls
. 
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cov(partisan_index) cl(geoid) report
{res}{title:DIFFERENCE-IN-DIFFERENCES WITH COVARIATES}

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{res}{p}Report - Covariates and coefficients:{p_end}
{txt}{hline 21}{c -}{hline 12}{c -}{hline 11}{c -}{hline 9}{c -}{hline 10}
 Variable(s)         {c |}   Coeff.   {c |} Std. Err. {c |}    t    {c |}  P>|t|
{hline 21}{c +}{hline 12}{c +}{hline 11}{c +}{hline 9}{c +}{hline 10}
{res}partisan_i~x{col 22}{txt}{c |} {res}-5.234{col 35}{txt}{c |} {res}0.404{col 47}{txt}{c |} {res}-12.964{col 57}{txt}{c |} {res}0.000
{txt}{hline 21}{c -}{hline 12}{c -}{hline 11}{c -}{hline 9}{c -}{hline 10}
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}60.569{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}65.730{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}5.161{col 27}{txt}{c |} {res}1.263{col 37}{txt}{c |} {res}4.09{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}60.357{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}66.469{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}6.112{col 27}{txt}{c |} {res}1.466{col 37}{txt}{c |} {res}4.17{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.951{col 27}{txt}{c |} {res}1.169{col 37}{txt}{c |} {res}0.81{col 47}{txt}{c |} {res}0.416
{txt}{hline 56}
R-square:{res}    0.33
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share (Partisan Index Control)) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cov(partisan_index) cl(geoid) report
{res}{title:DIFFERENCE-IN-DIFFERENCES WITH COVARIATES}

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{res}{p}Report - Covariates and coefficients:{p_end}
{txt}{hline 21}{c -}{hline 12}{c -}{hline 11}{c -}{hline 9}{c -}{hline 10}
 Variable(s)         {c |}   Coeff.   {c |} Std. Err. {c |}    t    {c |}  P>|t|
{hline 21}{c +}{hline 12}{c +}{hline 11}{c +}{hline 9}{c +}{hline 10}
{res}partisan_i~x{col 22}{txt}{c |} {res}-0.215{col 35}{txt}{c |} {res}0.006{col 47}{txt}{c |} {res}-33.597{col 57}{txt}{c |} {res}0.000
{txt}{hline 21}{c -}{hline 12}{c -}{hline 11}{c -}{hline 9}{c -}{hline 10}
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.692{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.735{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.043{col 27}{txt}{c |} {res}0.019{col 37}{txt}{c |} {res}2.23{col 47}{txt}{c |} {res}0.026**
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.745{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.870{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.125{col 27}{txt}{c |} {res}0.035{col 37}{txt}{c |} {res}3.62{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.082{col 27}{txt}{c |} {res}0.034{col 37}{txt}{c |} {res}2.41{col 47}{txt}{c |} {res}0.016**
{txt}{hline 56}
R-square:{res}    0.63
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Legislator Position (Partisan Index Control)) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cov(pvi08) cl(geoid) report
{res}{title:DIFFERENCE-IN-DIFFERENCES WITH COVARIATES}

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{res}{p}Report - Covariates and coefficients:{p_end}
{txt}{hline 21}{c -}{hline 12}{c -}{hline 11}{c -}{hline 9}{c -}{hline 10}
 Variable(s)         {c |}   Coeff.   {c |} Std. Err. {c |}    t    {c |}  P>|t|
{hline 21}{c +}{hline 12}{c +}{hline 11}{c +}{hline 9}{c +}{hline 10}
{res}pvi08{col 22}{txt}{c |} {res}0.813{col 35}{txt}{c |} {res}0.039{col 47}{txt}{c |} {res}21.115{col 57}{txt}{c |} {res}0.000
{txt}{hline 21}{c -}{hline 12}{c -}{hline 11}{c -}{hline 9}{c -}{hline 10}
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}46.429{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}46.806{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.377{col 27}{txt}{c |} {res}1.068{col 37}{txt}{c |} {res}0.35{col 47}{txt}{c |} {res}0.724
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}45.904{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}47.730{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}1.826{col 27}{txt}{c |} {res}1.287{col 37}{txt}{c |} {res}1.42{col 47}{txt}{c |} {res}0.157
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}1.449{col 27}{txt}{c |} {res}1.152{col 37}{txt}{c |} {res}1.26{col 47}{txt}{c |} {res}0.209
{txt}{hline 56}
R-square:{res}    0.56
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share (2008 PVI Control)) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) 
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment) cov(pvi08) cl(geoid) report
{res}{title:DIFFERENCE-IN-DIFFERENCES WITH COVARIATES}

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{res}{p}Report - Covariates and coefficients:{p_end}
{txt}{hline 21}{c -}{hline 12}{c -}{hline 11}{c -}{hline 9}{c -}{hline 10}
 Variable(s)         {c |}   Coeff.   {c |} Std. Err. {c |}    t    {c |}  P>|t|
{hline 21}{c +}{hline 12}{c +}{hline 11}{c +}{hline 9}{c +}{hline 10}
{res}pvi08{col 22}{txt}{c |} {res}0.025{col 35}{txt}{c |} {res}0.001{col 47}{txt}{c |} {res}31.129{col 57}{txt}{c |} {res}0.000
{txt}{hline 21}{c -}{hline 12}{c -}{hline 11}{c -}{hline 9}{c -}{hline 10}
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.070{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}-0.027{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}-0.096{col 27}{txt}{c |} {res}0.028{col 37}{txt}{c |} {res}-3.50{col 47}{txt}{c |} {res}0.001***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.109{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.122{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.012{col 27}{txt}{c |} {res}0.030{col 37}{txt}{c |} {res}0.41{col 47}{txt}{c |} {res}0.679
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.109{col 27}{txt}{c |} {res}0.036{col 37}{txt}{c |} {res}3.05{col 47}{txt}{c |} {res}0.002***
{txt}{hline 56}
R-square:{res}    0.62
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Legislator Position (2008 PVI Control)) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. // Including Pretreatment Partisan Index in Propensity Score Estimation
. psmatch2 tp3interacted white_pct median_income median_age density_num partisan_index, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,915
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    292.61
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2470.1124{txt}{col 49}Pseudo R2{col 67}= {res}    0.0559

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} tp3interacted{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}white_pct {c |}{col 16}{res}{space 2} .0026853{col 28}{space 2} .0006514{col 39}{space 1}    4.12{col 48}{space 3}0.000{col 56}{space 4} .0014085{col 69}{space 3}  .003962
{txt}{space 1}median_income {c |}{col 16}{res}{space 2}-3.20e-06{col 28}{space 2} 1.38e-06{col 39}{space 1}   -2.32{col 48}{space 3}0.020{col 56}{space 4}-5.91e-06{col 69}{space 3}-4.98e-07
{txt}{space 4}median_age {c |}{col 16}{res}{space 2} .0467974{col 28}{space 2} .0061251{col 39}{space 1}    7.64{col 48}{space 3}0.000{col 56}{space 4} .0347924{col 69}{space 3} .0588025
{txt}{space 3}density_num {c |}{col 16}{res}{space 2}-.0065292{col 28}{space 2} .0123975{col 39}{space 1}   -0.53{col 48}{space 3}0.598{col 56}{space 4}-.0308278{col 69}{space 3} .0177695
{txt}partisan_index {c |}{col 16}{res}{space 2}-.1544114{col 28}{space 2} .0132304{col 39}{space 1}  -11.67{col 48}{space 3}0.000{col 56}{space 4}-.1803425{col 69}{space 3}-.1284803
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.8502958{col 28}{space 2}  .233806{col 39}{space 1}   -3.64{col 48}{space 3}0.000{col 56}{space 4}-1.308547{col 69}{space 3}-.3920444
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 51.4964104   40.3486037   11.1478066   .468391507    23.80
{txt}{col 17}        ATT {c |}{res} 51.4820059   47.0336832    4.4483227   .767954227     5.79
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .148955268  -.098913329   .247868597   .014249249    17.40
{txt}{col 17}        ATT {c |}{res} .148290742   .087280268   .061010473    .02108441     2.89
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0      1,523 {txt}{c |}{res}     1,523 
{txt}   Treated {c |}{res}         5      2,387 {txt}{c |}{res}     2,392 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}         5      3,910 {txt}{c |}{res}     3,915 
{txt}
{com}. drop iwps3_treated_index
{txt}
{com}. gen iwps3_treated_index = 1/_pscore if _treated == 1
{txt}(1,523 missing values generated)

{com}. recode iwps3_treated_index (.=0)
{txt}(iwps3_treated_index: 1523 changes made)

{com}. drop iwps3_untreated_index
{txt}
{com}. gen iwps3_untreated_index = 1/(1-_pscore) if _treated == 0
{txt}(2,392 missing values generated)

{com}. recode iwps3_untreated_index (. = 0)
{txt}(iwps3_untreated_index: 2392 changes made)

{com}. drop iwps3_index
{txt}
{com}. gen iwps3_index = iwps3_treated_index + iwps3_untreated_index
{txt}
{com}. 
. diff recent_presidential_share if year == 2008 | year ==2016 [aweight = iwps3_index], t(tp3interacted) p(posttreatment) cl(geoid) // non-sig but directionally aligned

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}43.261{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}48.116{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}4.855{col 27}{txt}{c |} {res}1.503{col 37}{txt}{c |} {res}3.23{col 47}{txt}{c |} {res}0.001***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}42.903{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}49.376{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}6.474{col 27}{txt}{c |} {res}1.856{col 37}{txt}{c |} {res}3.49{col 47}{txt}{c |} {res}0.001***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}1.619{col 27}{txt}{c |} {res}1.230{col 37}{txt}{c |} {res}1.32{col 47}{txt}{c |} {res}0.189
{txt}{hline 56}
R-square:{res}    0.03
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share (Partisan Index Weighting)) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year == 2008 | year ==2016 [aweight = iwps3_index], t(tp3interacted) p(posttreatment) cl(geoid) // remains sig

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.024{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.010{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.034{col 27}{txt}{c |} {res}0.045{col 37}{txt}{c |} {res}0.76{col 47}{txt}{c |} {res}0.447
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.025{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.170{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.145{col 27}{txt}{c |} {res}0.048{col 37}{txt}{c |} {res}3.03{col 47}{txt}{c |} {res}0.003***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.111{col 27}{txt}{c |} {res}0.044{col 37}{txt}{c |} {res}2.51{col 47}{txt}{c |} {res}0.012**
{txt}{hline 56}
R-square:{res}    0.03
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Legislator Position (Partisan Index Weighting)) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. // Including 2008 PVI in Propensity Score Estimation
. psmatch2 tp3interacted white_pct median_income median_age density_num pvi08, out(recent_presidential_share district_nokkenpoole) common 
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     3,915
{txt}{col 49}LR chi2({res}5{txt}){col 67}= {res}    612.67
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2310.0846{txt}{col 49}Pseudo R2{col 67}= {res}    0.1171

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}tp3interacted{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}white_pct {c |}{col 15}{res}{space 2} .0007436{col 27}{space 2} .0006746{col 38}{space 1}    1.10{col 47}{space 3}0.270{col 55}{space 4}-.0005786{col 68}{space 3} .0020659
{txt}median_income {c |}{col 15}{res}{space 2}-8.37e-07{col 27}{space 2} 1.42e-06{col 38}{space 1}   -0.59{col 47}{space 3}0.554{col 55}{space 4}-3.61e-06{col 68}{space 3} 1.94e-06
{txt}{space 3}median_age {c |}{col 15}{res}{space 2} .0452482{col 27}{space 2}  .006249{col 38}{space 1}    7.24{col 47}{space 3}0.000{col 55}{space 4} .0330004{col 68}{space 3}  .057496
{txt}{space 2}density_num {c |}{col 15}{res}{space 2} .0110682{col 27}{space 2} .0126478{col 38}{space 1}    0.88{col 47}{space 3}0.382{col 55}{space 4}-.0137211{col 68}{space 3} .0358575
{txt}{space 8}pvi08 {c |}{col 15}{res}{space 2} .0333926{col 27}{space 2} .0016342{col 38}{space 1}   20.43{col 47}{space 3}0.000{col 55}{space 4} .0301896{col 68}{space 3} .0365955
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-1.378108{col 27}{space 2}  .230829{col 38}{space 1}   -5.97{col 47}{space 3}0.000{col 55}{space 4}-1.830524{col 68}{space 3}-.9256913
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}{hline 28}{c TT}{hline 59}
        Variable     Sample {c |}    Treated     Controls   Difference         S.E.   T-stat
{hline 28}{c +}{hline 59}
recent_preside~e  Unmatched {c |}{res} 51.4964104   40.3486037   11.1478066   .468391507    23.80
{txt}{col 17}        ATT {c |}{res} 51.4398925   50.3032948   1.13659774   .692702547     1.64
{txt}{hline 28}{c +}{hline 59}
district_nokke~e  Unmatched {c |}{res} .148955268  -.098913329   .247868597   .014249249    17.40
{txt}{col 17}        ATT {c |}{res} .147026857   .124067142   .022959715   .020807562     1.10
{txt}{hline 28}{c +}{hline 59}
Note: S.E. does not take into account that the propensity score is estimated.

 psmatch2: {c |}   psmatch2: Common
 Treatment {c |}        support
assignment {c |} Off suppo  On suppor {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
 Untreated {c |}{res}         0      1,523 {txt}{c |}{res}     1,523 
{txt}   Treated {c |}{res}         9      2,383 {txt}{c |}{res}     2,392 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}         9      3,906 {txt}{c |}{res}     3,915 
{txt}
{com}. psgraph
{res}{txt}
{com}. pstest white_pct median_income

{txt}{hline 24}{c TT}{hline 26}{c TT}{hline 15}{c TT}{hline 10}
        {col 24} {c |}       Mean               {c |}     t-test    {c |}  V(T)/
Variable{col 24} {c |} Treated Control    %bias {c |}    t    p>|t| {c |}  V(C)
{hline 24}{c +}{hline 26}{c +}{hline 15}{c +}{hline 10}
white_pct             {col 24} {c |}{res} 47.908   47.054      2.4{txt} {c |}{res}   0.81  0.418{txt} {c |}{res}  0.99
{txt}median_income         {col 24} {c |}{res}  55020    54876      0.9{txt} {c |}{res}   0.32  0.746{txt} {c |}{res}  0.97
{txt}{hline 24}{c BT}{hline 26}{c BT}{hline 15}{c BT}{hline 10}
* if variance ratio outside [0.92; 1.08]

{hline 70}
Ps R2   LR chi2   p>chi2   MeanBias   MedBias      B       R     %Var 
{hline 70}
{res}0.000      0.67    0.714      1.7       1.7       2.4    1.02{col 67}  0
{txt}{hline 70}
* if B>25%, R outside [0.5; 2]

{com}. drop iwps3_treated_granular
{txt}
{com}. gen iwps3_treated_granular = 1/_pscore if _treated == 1
{txt}(1,523 missing values generated)

{com}. recode iwps3_treated_granular (.=0)
{txt}(iwps3_treated_granular: 1523 changes made)

{com}. drop iwps3_untreated_granular
{txt}
{com}. gen iwps3_untreated_granular = 1/(1-_pscore) if _treated == 0
{txt}(2,392 missing values generated)

{com}. recode iwps3_untreated_granular (. = 0)
{txt}(iwps3_untreated_granular: 2392 changes made)

{com}. drop iwps3_granular
{txt}
{com}. gen iwps3_granular = iwps3_treated_granular + iwps3_untreated_granular
{txt}
{com}. 
. diff recent_presidential_share if year == 2008 | year ==2016 [aweight = iwps3_granular], t(tp3interacted) p(posttreatment) cl(geoid) // non-sig but directionally aligned

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}45.681{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}46.920{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}1.238{col 27}{txt}{c |} {res}1.626{col 37}{txt}{c |} {res}0.76{col 47}{txt}{c |} {res}0.447
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}45.203{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}48.052{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}2.849{col 27}{txt}{c |} {res}1.891{col 37}{txt}{c |} {res}1.51{col 47}{txt}{c |} {res}0.133
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}1.611{col 27}{txt}{c |} {res}1.272{col 37}{txt}{c |} {res}1.27{col 47}{txt}{c |} {res}0.206
{txt}{hline 56}
R-square:{res}    0.01
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share (2008 PVI Weighting)) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. diff district_nokkenpoole if year == 2008 | year ==2016 [aweight = iwps3_granular], t(tp3interacted) p(posttreatment) cl(geoid) // non-sig but directionally aligned

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}171{col 28}167{txt}{col 40}338
   Treated:{res}{col 13}264{col 28}268{txt}{col 40}532
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.023{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}-0.010{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}-0.033{col 27}{txt}{c |} {res}0.047{col 37}{txt}{c |} {res}-0.72{col 47}{txt}{c |} {res}0.475
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.097{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.133{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.036{col 27}{txt}{c |} {res}0.050{col 37}{txt}{c |} {res}0.73{col 47}{txt}{c |} {res}0.468
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.070{col 27}{txt}{c |} {res}0.046{col 37}{txt}{c |} {res}1.52{col 47}{txt}{c |} {res}0.130
{txt}{hline 56}
R-square:{res}    0.02
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Legislator Position (2008 PVI Weighting)) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. /// Placebo Tests ///
> 
. // Placebo Test with Alternative Treatment (2008 Partisanship)
. //H1
. diff recent_presidential_share if year ==2008 | year==2016 [aweight = iwps3], t(rep08) p(posttreatment) cl(geoid) // no movement H1

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}257{col 28}252{txt}{col 40}509
   Treated:{res}{col 13}178{col 28}183{txt}{col 40}361
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}39.461{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}53.269{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}13.808{col 27}{txt}{c |} {res}1.342{col 37}{txt}{c |} {res}10.29{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}39.238{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}53.975{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}14.738{col 27}{txt}{c |} {res}1.631{col 37}{txt}{c |} {res}9.04{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.930{col 27}{txt}{c |} {res}0.871{col 37}{txt}{c |} {res}1.07{col 47}{txt}{c |} {res}0.286
{txt}{hline 56}
R-square:{res}    0.20
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. collapse (mean) recent_presidential_share (sem) se_recent_presidential_share = recent_presidential_share [aweight = iwps3], by(year rep08)
{txt}
{com}. drop if year == 2018 | year == 2006 | year == 2010 | year == 2014
{txt}(8 observations deleted)

{com}. gen upper_ci = recent_presidential_share + (se_recent_presidential_share*1.96)
{txt}
{com}. gen lower_ci = recent_presidential_share - (se_recent_presidential_share*1.96)
{txt}
{com}. reshape wide recent_presidential_share se_recent_presidential_share upper_ci lower_ci, i(year) j(rep08)
{txt}(note: j = 0 1)

Data{col 36}long{col 43}->{col 48}wide
{hline 77}
Number of obs.                 {res}      10   {txt}->{res}       5
{txt}Number of variables            {res}       6   {txt}->{res}       9
{txt}j variable (2 values)             {res}rep08   {txt}->   (dropped)
xij variables:
              {res}recent_presidential_share   {txt}->   {res}recent_presidential_share0 recent_presidential_share1
           se_recent_presidential_share   {txt}->   {res}se_recent_presidential_share0 se_recent_presidential_share1
                               upper_ci   {txt}->   {res}upper_ci0 upper_ci1
                               lower_ci   {txt}->   {res}lower_ci0 lower_ci1
{txt}{hline 77}

{com}. graph twoway (line recent_presidential_share0 year, lcolor(gs6) lpattern(dash) lwidth(thick)) (rcap upper_ci0 lower_ci0 year, lcolor(gs6) lpattern(dash) lwidth(thin)) (line recent_presidential_share1 year, lcolor(gs2) lwidth(thick)) (rcap upper_ci1 lower_ci1 year, lcolor(gs2) lwidth(thin)), legend(order(1 "Control" 3 "Treatment") title("Group", size(small))) ytitle("Presidential Vote Share", size(small)) xtitle("Year (Treatment Period: 2010-2014)", size(small)) ylabel(40(5)60) xlabel(2000(4)2016) xline(2012, lwidth(30) lcolor(gs14%50) lpattern(solid)) // Figure C1
{res}{txt}
{com}. 
. //H2
. use "Blum & Cowburn Data.dta", clear
{txt}
{com}. drop placebo_h2
{txt}
{com}. gen placebo_h2 = 1 if pvi08 > 0 
{txt}(1,825 missing values generated)

{com}. recode placebo_h2 (.=0)
{txt}(placebo_h2: 1825 changes made)

{com}. diff district_nokkenpoole if year ==2008 | year==2016 [aweight = iwps3], t(placebo_h2) p(posttreatment) cl(geoid) // no movement H2

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}205{col 28}200{txt}{col 40}405
   Treated:{res}{col 13}230{col 28}235{txt}{col 40}465
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.361{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.292{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.653{col 27}{txt}{c |} {res}0.031{col 37}{txt}{c |} {res}21.37{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}-0.289{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.398{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.687{col 27}{txt}{c |} {res}0.032{col 37}{txt}{c |} {res}21.73{col 47}{txt}{c |} {res}0.000***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.034{col 27}{txt}{c |} {res}0.037{col 37}{txt}{c |} {res}0.92{col 47}{txt}{c |} {res}0.356
{txt}{hline 56}
R-square:{res}    0.56
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Legislator Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. collapse (mean) district_nokkenpoole (sem) se_district_nokkenpoole = district_nokkenpoole [aweight = iwps3], by(year placebo_h2)
{txt}
{com}. drop if year == 2000 | year == 2018
{txt}(4 observations deleted)

{com}. gen upper_ci = district_nokkenpoole + (se_district_nokkenpoole*1.96)
{txt}
{com}. gen lower_ci = district_nokkenpoole - (se_district_nokkenpoole*1.96)
{txt}
{com}. reshape wide district_nokkenpoole se_district_nokkenpoole upper_ci lower_ci, i(year) j(placebo_h2)
{txt}(note: j = 0 1)

Data{col 36}long{col 43}->{col 48}wide
{hline 77}
Number of obs.                 {res}      14   {txt}->{res}       7
{txt}Number of variables            {res}       6   {txt}->{res}       9
{txt}j variable (2 values)        {res}placebo_h2   {txt}->   (dropped)
xij variables:
                   {res}district_nokkenpoole   {txt}->   {res}district_nokkenpoole0 district_nokkenpoole1
                se_district_nokkenpoole   {txt}->   {res}se_district_nokkenpoole0 se_district_nokkenpoole1
                               upper_ci   {txt}->   {res}upper_ci0 upper_ci1
                               lower_ci   {txt}->   {res}lower_ci0 lower_ci1
{txt}{hline 77}

{com}. replace year = 109 in 1
{txt}(1 real change made)

{com}. replace year = 110 in 2
{txt}(1 real change made)

{com}. replace year = 111 in 3
{txt}(1 real change made)

{com}. replace year = 112 in 4
{txt}(1 real change made)

{com}. replace year = 113 in 5
{txt}(1 real change made)

{com}. replace year = 114 in 6
{txt}(1 real change made)

{com}. replace year = 115 in 7
{txt}(1 real change made)

{com}. graph twoway (line district_nokkenpoole0 year, lcolor(gs6) lpattern(dash) lwidth(thick)) (rcap upper_ci0 lower_ci0 year, lcolor(gs6) lpattern(dash) lwidth(thin)) (line district_nokkenpoole1 year, lcolor(gs2) lwidth(thick)) (rcap upper_ci1 lower_ci1 year, lcolor(gs2) lwidth(thin)), legend(order(1 "Control" 3 "Treatment") title("Group", size(small))) ytitle("Legislator Position", size(small)) xtitle("Congress (Treatment Period: 112th–114th Congress)", size(small)) xlabel(109(1)115) xline(113, lwidth(50) lcolor(gs14%50) lpattern(solid)) // Figure C2
{res}{txt}
{com}. 
. // Placebo Test with Random Dates
. use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. diff recent_presidential_share if year_placebo ==2008 | year_placebo ==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment_placebo) cl(geoid) // year_placebo generated using shufflevar to randomly assign dates

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}179{col 28}164{txt}{col 40}343
   Treated:{res}{col 13}256{col 28}271{txt}{col 40}527
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}recen~e{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}44.220{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}50.827{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}6.607{col 27}{txt}{c |} {res}1.675{col 37}{txt}{c |} {res}3.94{col 47}{txt}{c |} {res}0.000***
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}44.608{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}50.772{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}6.164{col 27}{txt}{c |} {res}1.839{col 37}{txt}{c |} {res}3.35{col 47}{txt}{c |} {res}0.001***
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}-0.443{col 27}{txt}{c |} {res}1.726{col 37}{txt}{c |} {res}0.26{col 47}{txt}{c |} {res}0.798
{txt}{hline 56}
R-square:{res}    0.05
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Pres Vote Share) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3) replace 
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. collapse (mean) recent_presidential_share (sem) se_recent_presidential_share = recent_presidential_share [aweight = iwps3], by(year_placebo tp3interacted)
{txt}
{com}. drop if year_placebo == 2018 | year == 2006 | year == 2010 | year == 2014
{txt}(8 observations deleted)

{com}. gen upper_ci = recent_presidential_share + (se_recent_presidential_share*1.96)
{txt}
{com}. gen lower_ci = recent_presidential_share - (se_recent_presidential_share*1.96)
{txt}
{com}. reshape wide recent_presidential_share se_recent_presidential_share upper_ci lower_ci, i(year) j(tp3interacted)
{txt}(note: j = 0 1)

Data{col 36}long{col 43}->{col 48}wide
{hline 77}
Number of obs.                 {res}      10   {txt}->{res}       5
{txt}Number of variables            {res}       6   {txt}->{res}       9
{txt}j variable (2 values)     {res}tp3interacted   {txt}->   (dropped)
xij variables:
              {res}recent_presidential_share   {txt}->   {res}recent_presidential_share0 recent_presidential_share1
           se_recent_presidential_share   {txt}->   {res}se_recent_presidential_share0 se_recent_presidential_share1
                               upper_ci   {txt}->   {res}upper_ci0 upper_ci1
                               lower_ci   {txt}->   {res}lower_ci0 lower_ci1
{txt}{hline 77}

{com}. graph twoway (line recent_presidential_share0 year, lcolor(gs6) lpattern(dash) lwidth(thick)) (rcap upper_ci0 lower_ci0 year, lcolor(gs6) lpattern(dash) lwidth(thin)) (line recent_presidential_share1 year, lcolor(gs2) lwidth(thick)) (rcap upper_ci1 lower_ci1 year, lcolor(gs2) lwidth(thin)), legend(order(1 "Control" 3 "Treatment") title("Group", size(small))) ytitle("Presidential Vote Share", size(small)) xtitle("Year (Treatment Period: 2010-2014)", size(small)) ylabel(40(5)55) xlabel(2000(4)2016) xline(2012, lwidth(30) lcolor(gs14%50) lpattern(solid)) // Figure C3
{res}{txt}
{com}. 
. use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. diff district_nokkenpoole if year_placebo ==2008 | year_placebo==2016 [aweight = iwps3], t(tp3interacted) p(posttreatment_placebo) cl(geoid) 

{txt}{title:DIFFERENCE-IN-DIFFERENCES ESTIMATION RESULTS}
{p}Number of observations in the DIFF-IN-DIFF:{res} 870{res}{p_end}
{txt}            Before         After    
   Control:{res}{col 13}179{col 28}164{txt}{col 40}343
   Treated:{res}{col 13}256{col 28}271{txt}{col 40}527
{col 13}435{col 28}435
{hline 56}
 Outcome var.   {c |} {res}dist~le{col 27}{txt}{c |} S. Err. {c |}   |t|   {c |}  P>|t|
{hline 16}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}{c +}{hline 9}
Before  {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.026{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.093{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.067{col 27}{txt}{c |} {res}0.053{col 37}{txt}{c |} {res}1.26{col 47}{txt}{c |} {res}0.207
{txt}After    {col 17}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
   Control{col 17}{c |} {com}0.015{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Treated{col 17}{c |} {com}0.134{col 27}{txt}{c |} {com}{col 37}{txt}{c |} {com}{col 47}{txt}{c |} 
   Diff (T-C){col 17}{c |} {res}0.119{col 27}{txt}{c |} {res}0.056{col 37}{txt}{c |} {res}2.11{col 47}{txt}{c |} {res}0.035**
{col 17}{txt}{c |} {col 27}{c |} {col 37}{c |} {col 47}{c |} 
Diff-in-Diff{col 17}{c |} {res}0.052{col 27}{txt}{c |} {res}0.061{col 37}{txt}{c |} {res}0.85{col 47}{txt}{c |} {res}0.394
{txt}{hline 56}
R-square:{res}    0.01
{txt}* Means and Standard Errors are estimated by linear regression
**Clustered Std. Errors
**Inference: *** p<0.01; ** p<0.05; * p<0.1

{com}. outreg2 using table_diff, ctitle(Legislator Position) addstat(Mean Control 2008, r(mean_c0), SE Control 2008, r(se_c0), Mean Treated 2008, r(mean_t0), SE Treated 2008, r(se_t0), Diff 2008, r(diff0), Diff 2008 SE, r(se_d0), Mean Control 2016, r(mean_c1), SE Control 2016, r(se_c1), Mean Treated 2016, r(mean_t1), SE Treated 2016, r(se_t1), Diff 2016, r(diff1), Diff 2016 SE, r(se_d1)) label word nocons dec(3)
{txt}{stata `"shellout using `"table_diff.rtf"'"':table_diff.rtf}
{browse `"C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries"' :dir}{com} : {txt}{stata `"seeout using "table_diff.txt", label"':seeout}

{com}. 
. use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. collapse (mean) district_nokkenpoole (sem) se_district_nokkenpoole = district_nokkenpoole [aweight = iwps3], by(year_placebo tp3interacted)
{txt}
{com}. drop if year_placebo == 2000 | year == 2018
{txt}(4 observations deleted)

{com}. gen upper_ci = district_nokkenpoole + (se_district_nokkenpoole*1.96)
{txt}
{com}. gen lower_ci = district_nokkenpoole - (se_district_nokkenpoole*1.96)
{txt}
{com}. reshape wide district_nokkenpoole se_district_nokkenpoole upper_ci lower_ci, i(year_placebo) j(tp3interacted)
{txt}(note: j = 0 1)

Data{col 36}long{col 43}->{col 48}wide
{hline 77}
Number of obs.                 {res}      14   {txt}->{res}       7
{txt}Number of variables            {res}       6   {txt}->{res}       9
{txt}j variable (2 values)     {res}tp3interacted   {txt}->   (dropped)
xij variables:
                   {res}district_nokkenpoole   {txt}->   {res}district_nokkenpoole0 district_nokkenpoole1
                se_district_nokkenpoole   {txt}->   {res}se_district_nokkenpoole0 se_district_nokkenpoole1
                               upper_ci   {txt}->   {res}upper_ci0 upper_ci1
                               lower_ci   {txt}->   {res}lower_ci0 lower_ci1
{txt}{hline 77}

{com}. replace year = 109 in 1
{txt}(1 real change made)

{com}. replace year = 110 in 2
{txt}(1 real change made)

{com}. replace year = 111 in 3
{txt}(1 real change made)

{com}. replace year = 112 in 4
{txt}(1 real change made)

{com}. replace year = 113 in 5
{txt}(1 real change made)

{com}. replace year = 114 in 6
{txt}(1 real change made)

{com}. replace year = 115 in 7
{txt}(1 real change made)

{com}. graph twoway (line district_nokkenpoole0 year_placebo, lcolor(gs6) lpattern(dash) lwidth(thick)) (rcap upper_ci0 lower_ci0 year_placebo, lcolor(gs6) lpattern(dash) lwidth(thin)) (line district_nokkenpoole1 year_placebo, lcolor(gs2) lwidth(thick)) (rcap upper_ci1 lower_ci1 year_placebo, lcolor(gs2) lwidth(thin)), legend(order(1 "Control" 3 "Treatment") title("Group", size(small))) ytitle("Legislator Position", size(small)) xtitle("Congress (Treatment Period: 112th–114th Congress)", size(small)) xlabel(109(1)115) xline(113, lwidth(50) lcolor(gs14%50) lpattern(solid)) // Figure C4
{res}{txt}
{com}. 
. use "Blum & Cowburn Data.dta", clear 
{txt}
{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Mike Cowburn\Documents\Other\Journal Submissions\Tea Party Primaries\Blum & Cowburn - Log File.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 9 Apr 2023, 17:25:03
{txt}{.-}
{smcl}
{txt}{sf}{ul off}